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.
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Data migration with DataRocket
DataRocket migrates data easily and efficiently from system A to B. Subsequently, the innovative, continuous consistency checks ensure compliance with the transformation rules â€“ the guarantee for high data quality in your target system!
DataRocket supports an iterative approach as well as writing data to the target system at the push of a button: plannable and also in subsets.
The smart consolidation workflow for data harmonization includes the merging of data from different source systems, the removal of duplicate content and the enrichment of incomplete information.
Our software DataRocket performs consistency checks and thus secures the migration results. For you, this means that incorrectly migrated data stocks are avoided.
Beyond the pure migration from system A to B, DataRocket offers a sustainable improvement of data quality. You benefit from the fact that only the appropriate data is migrated to your target system.
The data migration process with DataRocket
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 goes one step further than comparable systems with a consistency check following the mapping. This step ensures a sustainable increase in data quality. DataRocket continuously checks the data records from both systems for consistency and cleans up inconsistent data.
This process will guarantee high data quality in your target systems!