Everything about data migration challenges and their solutions
Posted: Jul 26, 2021
Technical Standards and evolving technology are the need of the hour. They do need to be built and keep evolving with technology as the requirements in the business are often the main directions of the migration. These projects not only demand cost but are also resource-intensive, like labour-intensive, error-prone, time-consuming, but also the requirement behind it is to be meticulously planned, with appropriate tools, and to achieve success demands intensive testing.
Data migration (DM) is a process of transfer of data or information from one system to another, which involves the transfer from a legacy NFS source system into currently utilizable systems or a new system, known as the targeted system or the application software. For outstanding revenue generation, the IT budget for any major company is generated when there is a combined performance of the data migration process and utilization of the resources in a DM project.WHY DATA MIGRATION IS PERFORMED1. Acquisition and merger of a business unit/organization that triggers process change in the organization.
- To improve the efficiency, performance, and scalability of software applications.
- To adopt new changes in terms of technology, Market practice, operational efficiency, regulatory requirement results in better customer service.
- The major cost-reduction is done by bringing down the operational expenses and efficiency by streamlining and removing the grid-lock process in the application procedure or when various data centers were relocated to cluster in a single location.
A proper comparison of the number of records in the legacy system and the target system will give a fair evaluation of the migrational data loss. The legacy and target system data won't match sometimes, but there are certain parameters in business rules which reject records based on the set parameters. Then the count of legacy system records is equal to the number of cancelled records plus the target system record count. Valid reasons should be put forward so as to cite the explanation for rejected records.
Key financial column reconciliation is the process of tracking the sum of all the columns which belong to key financial data or ex closing balance, tracking available balance, etc., and the comparison between legacy and target system, which shall result in data loss identification. If any mismatches are suspected, then they are corrected by digging all the old files, then it's at the granular level where all the mismatches and root cause behind the mistake is traced and analyzed to find the real reason behind such data losses.DATA CORRUPTION AND INTEGRITY BREACH:The content and the details according to a given format in legacy and target systems are compared. If the details obtained are different as compared to the migration process, then such data is termed " corrupted data." Due to data migration, mistakes, anomalies, irregularities, or abnormalities of various forms are observed in the data. Suppose the data is replaced with some useless or duplicate or presence of some senseless information, then it's a matter of data integrity affair with a variety of issues. Such type of corrupted data and data integrity affects the business and operation efficiency, and it totally beats the plan of data migration.Solution: Regular validation of data.The best methodology to avoid the corruption of data is by validating the authenticity of each and every data between the legacy and target system. The best ways to maintain the data validation methodologies are which are widely used are as follows:
- Validating sample data.
- Creating subsets of data validates
- Overall validation of data set thoroughly
- Project stability.
- Data coverage.
- Execution time.
- Efficiency of the targeted query/data script.
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