At some time or another, all companies arrive at a point when new applications must be introduced or data transferred to other systems, e.g., within the framework of the introduction of new ERP and CRM solutions or Legacy systems. Consolidation of data from heterogeneous IT systems with widely varying source and target structures presents a major challenge. Poor data quality caused by duplicates, inaccurate or incorrect data, or differing data structures in source and target systems, all make the data migration project – and thus the working effectiveness with the new system – far more difficult. If these problems are first identified in the new system, this will affect acceptance by your employees. Therefore, attention must be paid to ensuring high quality data immediately during data migration - if not - the data migration project will be endangered.
In many cases, the complexity and magnitude of data migration is underestimated. Our experience shows that the actual demands on data made by processes are often neglected. But questions can arise, such as:
We examine and answer these questions together with the specialist departments involved, and use the information gained to decide upon a firm migration plan. Together we develop the migration scenario most suitable for you needs and requirements, and where optimising data quality has the highest priority. We successfully implement your migration project with tried-and-tested best practice methods. In time - and within budget.
Poor data quality is frequently cause of problems with data migration projects, and presents challenges such as:
The complexity and magnitude of data migration is often underestimated. Uniserv has developed Best Practice methods which have been tried and tested many times, and with which you can implement data migration projects successfully in time, and within budget. The 5 steps are:
In the first step, a rough project plan is drawn up in which the appropriate stakeholders with their roles are defined, the company target is set, and an overview of source and target systems is made.
Precise procedures for the different project phases are decided during the data migration concept. Also, the source system and concrete data migration strategy is determined, a risk analysis is made, test scenarios performed, and rollback scenarios defined.
3. Data Cleansing Stream: preparation and data quality of source systems
To ensure that only clearly defined and unified datasets enter the target system, a set of data quality rules for the business areas affected is elaborated in workshops. It is determined how far the quality of data in the source system reaches the demands of daily business. Measures for optimising and implementing data quality are defined and initialised.
4. Migration Stream: transformation rules and data quality in the target system
At the start of the fourth project phase, the design of the target system must be made clear and the form of data transformation decided. These directives are then technically implemented by the software.
5. Build, Test, and Go Live
The actual data migration is technically implemented. Data is extracted from the source systems, adapted to the quality demands of the target system by using different transformation components, and finally loaded into the target system.
Securing sustainable data quality
Once high quality data is in the target system - users will be satisfied. To ensure that the data quality level remains high, we recommend installing a data quality firewall in the target system to avoid data becoming polluted in the future.