With the evolution of technology and standards, the requirements of a business also change with time. This often encounters the need for migration. Intensive testing, finding the right tools and strategic planning are a must for the success of the data migration services.
Migration of data refers to the procedure to transfer the data from one system to the other. It involves the movement of the data into the new system or the target system from the legacy source systems. Data migration is useful in bringing an improvement in the performance, scalability, and effectiveness of the software application. It also plays a vital role in reducing the efficiency and operational cost by streamlining and removal of bottleneck during the application process.
Challenges and steps of data migration to a successful enterprise solution
Here is a list of few of the risks which are associated with the process of data migration.
Risk of data loss
If the data is present in the legacy system or source but cannot be found in the target location after the migration of data, it is regarded as the data loss. It is associated with higher risks. Poor data enhance the reputation, thereby leading to financial risks.
Solution:
There are primarily two solutions to deal with data loss which are key financial column reconciliation and count reconciliation. Comparing the total count of records in the targeted system and the source will help in assessing the data loss count. However, it is not mandatory that the record count of the source and target system will match always. At times, the business may choose to reject specific records during migration based on specific parameters. In case these rules were made, the total count of records in the source should be the combination of record count in the target system plus the rejected record count.
Key financial column reconciliation is considered to be the recording of summation of different key financial columns, available balance, ex closing balance. Drawing a comparison between the target system and the source will be effective in understanding the loss of data. In case you encounter any sort of mismatch, you will be able to drill the same to the granular level for figuring out the mismatching records. Root cause analysis is conducted in such cases for finding specific reasons for loss of data.
Data integrity and data corruption
If the content and format of data in the target system and legacy system are found to be different in the comparison owing to the migration process, it is said that the data has been corrupted. Owing to the migration of data, redundant or anomalies or duplicated data, non meaningful data are relevant to the issues of data integrity. Data integrity and data corruption have an impact on the efficiency of the operation and business in a negative manner.
Solution:
Data validation is considered to be the perfect solution to this challenge. Validation of each data between the target and source contributes to being the best methodology for avoiding the corruption of data. Few of the methodologies of data validation are inclusive of subsets of data validation, sample data validation, and complete data set validation.
In Sample data analytics, the random record is chosen from the source after which it is compared with the data, present in the target system. Sampling is not regarded as a defective system as it chooses random records of small size. Profiling of the sample records is useful in fetching additional coverage of data, in comparison to the random samples.
Speaking of the subsets of data validation, here you need to select the subsets of records, according to the row number, in place of selecting the sample records for verification. The benefit of choosing this solution is that it allows you to choose more records and thus you can expect additional coverage of data, based on higher probability.
Complete data set validation refers to the perfect validation process in which each record is compared in a bidirectional way, viz., the records are checked in the target system against the legacy system and again the legacy system against the target system. Exception Query is known to be used here for comparing data present between the systems. Parameters that you need to take into account for the validation of data are inclusive of coverage of data, the stability of the project, and the projection time.
Interference risk
Such type of challenges is found to occur as every stakeholder make use of source application during the transition time simultaneously. For instance, in case a stakeholder gets access to the specific table after which he ends up in locking it, and in case another member of the team tries getting access to the table, he may fail to do so. Interference risks are found to be associated in similar conditions.
Solution:
It is essential to manage the same at the organizational level. It is a prerequisite that such scenarios should be discussed in the initial phase during the planning of the project. One option is planning the multiple mock runs which involve the stakeholders and plan the dry run in the pre-production environment along with the stakeholders.
Semantic risks
During the data migration process at times, the meaning of the target column and source column may be the same. However, the unit of measurement may be different and thus, the meaning of data may change completely. You need to keep in mind that in such cases, data is not corrupted or loss. However, the migration of data will not help achieve the desired objective.
Solution
Subject matter experts and real-time users should refer to a feasibility study which will be useful in detecting the semantic issues during the early phase of the project life cycle. The test scope should be inclusive of test cases for understanding the incompatibilities and inconsistency between the parameterization of the specific target application and the migrated data.
Bottom Line
Data Migration Solutions has become an integral part of its operations in the business environment of today. Any issue in the quality of data may lead to disruption in the operations of the business. For the prevention of such issues, the business firms require a trusted and consistent process which will be useful in planning, designing, validation, and migration of the data. If you’re making any drastic changes or improvements at your product or software, doesn’t it make sense to go with a company like Indium Software - Leading QA Solution Provider.
Thanks and Regards,
Bavana Princy
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