To maximize productivity and ensure a clean transition from older applications or systems a unifying data migration strategy is needed, one that enables an enterprise to address its business needs in the best way. SQL View’s enterprise data migration solutions offer both large and small organizations a fast, efficient, and cost-effective way to re-deploy data across hardware platforms, applications, and operating systems. Proper data migration is key in the implementation of a Record Management System as well as a Document Management System. This is to ensure that organisations can achieve high productivity upon system launch.
The key issues to identify are:
Is there a hard-stop date by which this migration must be complete?
What is the impact to the business if the project is not complete by the deadline?
What risks do you see with migrating the data (volume, multiple data sources, data quality issues, system access)?
How will the project be managed (project manager, team, steering committee) and who has the final say?
How will you measure the success of the data migration?
Identify Source and Target systems
Have the systems been in production for a period of time?
Define any validation rules for data extraction and data load (ie only extract open or active records, all data since 2008)
Define error handling (what to do if fields fail, ie fail the entire record or process partial records)
Define record ownership in target system(s)
Document testing approach
Document stakeholders and system owners
Identify key risks
Define success criteria
Data review and verification
Document data sources, versions, locations, accessibility
Define objects and fields to be migrated
Size of the data to be migrated
Extraction process, tools and resulting format
If you are going to do multiple data extractions, can you easily extract data deltas, ie extract for different time periods?
Data relationship and dependencies – determine the order of migration
Data quality, document any data cleansing and de-duplication required, determine if there needs to be an intermediary data store to make data cleansing easier
Data mapping and data transformation
Data load estimate
Business and Operational constraints
Document any business constraints (especially if you are dealing with production systems)
Impact to any support processes
Resource availability for source and target systems
Pre-migration test with sample data
Adjust time lines and tasks based on test
Pulling it all together
Time required and tasks for:
Data mapping and transformation
Data load (Calculate the time involved to migrate the data including any testing and fall back contingencies you need)
Customizations or changes required for target system