Don´t underestimate data migrationn in your next project.
Introducing new information systems means moving data. And that usually involves complicated transformations as you link data from multiple sources and resolve quality problems. Let us solve these problems for you with our proven frameworks.
Are you planning to implement a new information system or make a fundamental change in your existing system? Do you need to access information about your customers, products and processes in a centralized database?
In all likelihood this calls for a new multi-sourced data system to adjust and resolve errors as well as any shortcomings acceptable in current system that however cannot be entered into the new system. This is not a simple task as no data can be lost and neither can any errors be introduced during the transfer as future corrections of data in the new system can be complicated. Large IT projects are often plagued by poorly planned and poorly implemented data migration. Rest easy and use our proven solutions for data migration which significantly shorten the phase and reduce costs by as much as 30%.
60% of large IT projects involve data migration and only 60% of the migrations are completed on time. Don't underestimate data migration in your next project.
ADASTRA DATA MIGRATION FRAMEWORK
Adastra Data Migration Framework projects are a proven set of methodologies, processes and technological solutions that ensure efficient and reliable transformation, data transfer which ensure the complete verification of its accuracy and goals.
Adastra Data Migration Framework provides tools for custom data migration, their consolidation, cleaning, transformation and reconciliation. Includes support for change management in business logic, data structure, linking data from multiple systems and treatment of any poor quality in them.
- Analysis of the data structures - The fundamental starting point for any successful migration is a detailed analysis of the source structures in source and target systems to understand and identify differences between these structures and their interaction mapping.
- Analysis of the data lifecycle - Data in the source and target systems may differ not only in structure, but also in how to work with the data, their life cycle and related processes. Therefore, it is essential to do two-way mapping of the condition of the data and to ensure the consistency and the quality of the new data. There may be times during a migration when a new data system declines as it does not meet defined business rules.
- Removal of poor quality data - The goal of data migration is not to clean data as effectively as possible, but to transfer the data, including errors, as faithfully as possible. As the data needs to comply with the requirements of the new system, it must be determined which data is to be used for proper systems operations. These controls must be automated and guaranteed in migration tools. Data cleaning is done either automatically as part of the migration tool, or, errors can be identified, categorized, and chosen for manual cleaning. What must be prevented is the situation when new data cannot be loaded because of errors.
- Migration Process - The migration tool determines how strictly the migration is run, how often tests will be repeated and how the whole process is conducted and fine-tuned. Individual runs record all errors that arise and the weaknesses in internal control reports in addition to identifying data that must adapt to business rules or which do not meet the quality requirements. This rigorous process ensures all problems are identified and resolved.
- Testing and reconciliation of data - An essential component of Adastra Data Migration Framework is a detailed reconciliation check of the data accuracy where the compliance of data between input and output, and the migration between phases, is checked. The tool enables the reconciliation of data by defining the complex rules involved in the input systems where data usually have a different structure than in the target system. Comparing and checking the data at this point is far from an easy task.
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