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·         Creation of scripts for data readout

The collection of data readout requires an intelligent approach. Depending on the source, the amount of tables, the kind of usage of tables, the amount of data and the possibility of data inconsistencies different methods have to be taken into account.

·         Data preparation by combination

A modern programme that replaces the legacy system is consequently more comprehensive in its composition. That means the legacy system lacks of data required for the new system. Additional external data sources (Excel, Access, Text, ...) need to be combined to fulfill the demands.

·         Spotting inconsistencies

Inconsistencies of data define a degree of quality and can make the information worthless. The existence of inconsistencies can lead a programme to malfunction or to erroneous results or to misinterpretation of facts. Therefore they have to be spotted and eliminated.

·         Data mapping to adapt

Quite often the new system works with a coding of basic data that is not covered by the legacy system. Additionally new programme rules apply that differentiate between data what was common information before. Both requires either a simple or an even  more sophisticated mapping on conditions.

·         Data transfer into destination

The data transfer into destination implies disruption or even down time. The goal is to keep it as short as possible. Therefore a well-organised planning is essential for a smooth transition. This is done by an optimised sequencing of the transfer layouts and the load of static before dynamic data.

·         Protocol of the data transfer

A data migration is rarely done in only one cycle. To focus on the main problems a protocol is generated to track errors. The protocol keeps the transfer layouts, the number of data, the loading time, the number of errors and the kind of errors occured during the data transfer in a listed and summarised form.