DATA MIGRATION
Successfully moving data from system A to B: DATAROCKET migrates and cleans data.
In today’s business world, IT modernization projects, mergers and acquisitions are part of everyday business. This typically results in the challenge of transferring data from different sources such as ERP systems, Excel spreadsheets or CRM systems to a new target system. This process is called data migration.
Data migrations are one of the most common causes of poor data quality. Poor data quality leads to migration projects being delayed and exceeding their budget. Inaccurate or incomplete data leads to delays and budget overruns in migration projects and can even have a long-term impact on business performance. Careful planning and execution of data migration is essential to ensure a smooth and successful transition to the new system.
The best product for Data Migration:
THE DATA MIGRATION PROCESS
DATAROCKET Core handles the three essential steps of the ETL process* (Extract, Transform and Load) of data migration for you. The special feature of data migration with DataRocket is the continuous checking of your data’s consistency already during the migration. This so-called consistency check thus contributes to increasing the data quality already during the execution of a data migration.
1. Analysis of the source data
In order to achieve high data quality we first examine your source data. Based on the target data structure, we define quality criteria that the data sets should meet. Additionally, duplicates are identified. After this cleansing, you will receive perfect data quality for the migration into the planned target system.
2. Mapping in target data structure
Individual rules are used to match the data from the source system to the structure of the target system. This data transformation is called mapping. Once the transformation rules have been successfully implemented, the source data is pushed to the target system.
3. Consistency check
DATAROCKET Core goes one step further than comparable systems with a consistency check following the mapping. This step ensures a sustainable increase in data quality. DATAROCKET Core continuously checks the data records from both systems for consistency and cleans up inconsistent data.
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