The need for data migration usually results from the introduction of a new system and is usually part of a larger project. Perhaps your business is consolidating one or more legacy systems and replacing them or deploying an additional system for legacy data. Your ultimate goal is to improve organizational performance.
Problems occur when existing data is migrated to a new target application and it is found to contain inaccuracies, unknowns, and redundant and duplicate material. Often legacy systems are so outdated that the source data is inadequate in content and structure for the objectives of the new system. This can be true, even when the data appears adequate in the source system.
It’s not just a matter of moving data from one system to the other!
The fact is that without a thorough understanding of both the source system and target system, transferring data into a more sophisticated application nearly always amplifies the negative impact of incorrect or irrelevant data.
It’s essential not to perpetuate any hidden legacy problems, and to ensure that the data the populates the new system is fit for the purpose. Unless sufficient care is taken during data migration, organizations increase exposure to risk.
Typically, businesses spend a great deal of time and research choosing their new system and very little time ensuring that the transfer of data will go smoothly. When problems occur in the migration process, unexpected costs nearly always occur.
Prior planning for data migration prevents problems and unexpected costs.
The Paper Alternative team are experts in effective data migration procedure. First we map the data on the old system to the new system and create a plan for data extraction and data loading. The plan design relates old data formats to the new system’s formats and requirements, as well as to your current data requirements and workflow.
Automated and manual data cleaning is commonly performed during migration to improve data quality, eliminate redundant or obsolete information, and match the requirements of the new system. Various dData migration phases (design, extraction, cleansing, load, verification) may be repeated several times for before applications of moderate to high complexity are deployed.
After loading into the new system, results are subjected to data verification to determine whether data was accurately translated, is complete, and supports processes in the new system. During verification, there may be a need for a parallel run of both systems to identify areas of disparity and forestall erroneous data loss.