In this fast-paced, digitally-enabled world, enterprises have no choice but to move from legacy systems to modern systems to be productive and efficient. However, enterprises cannot simply abandon a system and move to another. Moving from one system to another has its own set of challenges, including retention of old data, and compatibility of the new system with the existing ecosystem. All data from the old system either needs to be carried forward to the new system or synchronized between old system and new system in a way that the teams have access to it from the new system itself. Data migration/synchronization, therefore, becomes the first step towards creation of a modern, agile ecosystem.
In this blog, we will briefly discuss the situations in which businesses need data migration, challenges involved in data migration, and why automated migration solutions are more reliable for migration of complex, enterprise-level data.
The Need For Data Migration
The world is moving more towards agility and cloud and therefore enterprise must:
- Migrate data to cloud to facilitate data access by geo-distributed teams
- Upgrade systems to meet new industry or compliance standards
- Consolidate multiple data sources into one and make it the source of truth for all teams
- Bring down the maintenance cost associated with old legacy systems while maintaining historical records in new systems for compliance reasons
For all the above activities to be successful, a clean, error free data migration process is necessary.
Challenges Involved In Data Migration
Enterprises choose either of the following ways of data migration:
- Exporting data from legacy system using excel sheets (if the new system provides that option)
- Transferring data using manual migration processes
- Transferring data using automated migration solutions
Most of the enterprises look for migration solutions that help them migrate data with intact models, structures, and history. Therefore, there are multiple factors that one must consider before settling for a migration process/solution.
Factors To Consider When Choosing A Migration Solution
By the time a company decides to migrate data from one system to another, they have a clear idea about the primary reasons for data migration. They also have a tentative budget in their mind. However, the challenge lies in choosing the right migration solution. While manual migration might sound enticing at the onset as it costs less, the long-term impact of manual migration could be disastrous if the following aspects are not thought through and analyzed.
The size of data that needs to be migrated: Manual migration suffices only for the cases in which small amount of data with not much significance for decision making is to be transferred from one system to another. In all other cases in which comments, attachments, history, and context of data is critical for business reasons, it is better to go with a sound, automated migration solution.
One point to another point migration or multi-points to one-point migration: Migration being one point to another point is something that can be achieved through manual migration. However, manual migration from multi-points to one-point can be an extremely challenging and error prone task. Automated migration rules out the possibility of manually induced errors, such as a user inadvertently changing the status of all ‘Active’ bugs to ‘Complete’ when copy pasting the data, which can be disastrous.
The types of systems involved in migration and the complexity of data to be migrated: Not all systems are same. For example, one system may support addition of objects in text fields whereas other system might not support it, or one system has rich-text based formatting whereas other system has Wiki-based formatting. Automated migration considers these modeling differences in the source and target systems and therefore, does not force fit data into two distinct projects. Therefore, the data in the target system is transferred in right format without any structural issues.
Timeframe that is available for migration: Automated migration is less-time consuming. For a large-size manual migration, an enterprise might have to put both the source and target systems on downtime for a long time and engage all the project resources to migrate data from one system to another. Whereas, migration solutions such as OpsHub Integration Manager support data migration between source and target systems without any system downtime.
Historical/Legal value of the data: From a compliance point of view, it is extremely critical to have all transaction records of all data present in the system currently in use. For example, in case of migrating from one development system to another, it is important to migrate the data with full-context around the user story lifecycle and traceability to its parent artefacts. Successful transfer of complete, contextual information (which also includes all the comments and attachments) also ensures that stakeholders do not have to keep going back to the older system for reference.
Operations downtime and updating data in new system while the migration is going on: With the high volume of data that any organization processes these days, it should not be astonishing if the time estimated for migration runs into a few weeks or even months. Locking users out of the system while migration process takes place, then, becomes an expensive proposition for any business. A good migration solution, therefore, should allow use of both the systems simultaneously and train the people in batches to use the new system. This would ensure that data migration is cost-effective, and non-disruptive for the business.
Why Automated Migration Has An Edge Over Manual Migration
The table below lists different the factors on which automated migration has an edge over manual migration.
Data migration is the first step towards a larger goal – leveraging a new system in the ecosystem. However, unfortunately, enterprises tend to follow an oversimplified, and sometimes, unplanned, or under-planned approach towards data migration. In a world moving towards automation and machine learning, enterprises must consider the long-term implications of any migration process they choose. While prima facie, copy-pasting data from one system to another look like an easy and cost-effective choice, it may prove not to be so in the long run.
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