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Data Conversion

Converting data from a legacy business technology, such as ERP, to a new one can be a challenging, lengthy and expensive endeavor that requires meticulous planning and execution. It entails a comprehensive approach to guarantee that all data is transferred and integrated accurately into the new system. Our teams provide a streamlined and efficient approach to handle the critical steps in the data conversion process efficiently:

Identify the data that needs to be converted: This step involves analyzing the data in the legacy applications and determining which data is relevant and needs to be converted to the new enterprise applications.

Map data: This step involves mapping the data from the legacy applications to the new ones. It is important to ensure that the data is correctly labeled and that all required fields are identified.

Create a data conversion plan: A comprehensive data conversion plan must consist of various critical elements, such as a conversion timeline, a testing program to verify the data quality after conversion, a contingency plan to address any issues that may surface, an alignment strategy with the overall implementation plan and the milestones that hinge on the completion of the data conversion process.

Conduct data cleansing: This step involves a meticulous examination of the data to eliminate any discrepancies or errors, ensuring that it is free of inconsistencies in advance of converting and integrating them to the new enterprise applications.

Test the data: After conversion, test the data based on validation procedures to ensure that the data being converted is accurate and complete. This can include cross-checking data in both systems, performing a data comparison between the source and target systems, and checking for data anomalies. Conduct sample testing of converted data to identify any issues and ensure that the mapping and reconciliation processes are working as expected.

Perform Data Reconciliation: Perform a final data reconciliation to ensure that all data has been accurately converted and that there are no data discrepancies between the two systems. This reconciliation should include validating data at the record level and checking for data volume, structure, and format.


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