Solution Description

ChainSys Smart Data Platform and proven methodology ensure data migration success, from any source to Microsoft Dynamics 365, in as little as one-third the time. Trust ChainSys Smart Data Platform to deliver your critical data migration project.

Use Cases, Key Features and Highlights

• Migrate master, reference, and transaction data from any source to Microsoft Dynamics 365.
  
• Consolidate multiple source systems and/or subsystems onto a single Microsoft Dynamics 365 instance.
  
• Support mergers, acquisitions, and divestitures in Microsoft Dynamics 365.
  
• Bifurcate a single Microsoft Dynamics 365 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
  

Why ChainSys

• Reduced Migration Project Risk and Timeline
  
• Smooth Cutovers with Reduced Cutover Time
  
• Satisfied Auditors
  

ChainSys Approach

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 Microsoft Dynamics 365, 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.