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Data migration is a significant part of implementing a new PLM system or upgrading an existing one. It is a critical step that can impact the success of the implementation or upgrade. The data migration process needs to be carefully planned and executed to ensure data integrity and minimize disruption to business operations.
It is possible for IT departments to be unfamiliar with a variety of applications and the way they function – for example, Agile PLM. The functionality of PLM may be provided by a hosting platform, an in-house server, virtual machines or even the cloud, but its deployment is discipline specific (Manufacturing and Engineering). It becomes increasingly necessary to replace these older, less functional PLM systems with newer, more advanced ones as time goes on.
For many managers and system administrators, this can be viewed as nothing short of disastrous. This process can be challenging due to the large volumes of data, different data formats, and the need to maintain business operations during the migration. They may not even have the time or resources to perform such delicate operations. Database schemas are different, character field text can become corrupt, and some existing data and relationships will simply be erroneous.
Without a well-planned data migration strategy, organizations can run into issues such as data corruption, loss, or leakage. This can lead to significant disruption in service, customer dissatisfaction, and can even result in fines for not being compliant with data regulations. It is essential for companies to have a comprehensive data migration plan in place before embarking on any project. Poor data migration can lead to significant operational costs and risks. It is important to have a clear plan for ensuring data migration is successful and secure.
A successful data migration can significantly impact PLM implementation. It can ensure data integrity, minimize downtime, and enable a smooth transition to the new system. Our process involves analyzing a representative data sample from the users PLM environment, using the sample export to configure the new environment. Data is parsed and read, then loaded into the target environment. Testing and validating the accuracy of the migration process and running the full migration process when we have received the complete production PLM data export
While the importance of data migration is clear, it’s not without its challenges.
Transforming native data between database schemas is easily done by using a standardized format or even formatted text. The conversion rules between schemas are usually very complex, involving different terminology and requiring splitting, aggregation, and substitution of data. This is to enable an object to be represented in the updated schema. Data from the source data objects must be described in an open structured format containing all the information, both current and historical.
Success in data migration can largely be determined by how prepared the owners of a specific application are. Things to consider include understanding what PLM does, where and how it delivers information to the application infrastructure at large (application dependencies), how and when users add data, the financial impact to the company, consistency, and prioritization.
Understanding these challenges is crucial, and we’ve compiled resources to help.
If you’re looking for more information on PLM migration, download our free eBook “An Introduction to PLM Migration” here
In addition to providing resources, we also believe in sharing best practices for PLM data migration. The good news is that through the use of best practices, current technology, and extensive experience, the task of data migration can be successful. The process of migrating data can be broken down into a series of tasks, metrics, and procedures that reduce costs and time to completion. This eliminates error and business manager headaches.
We’ve created our own unique Migration parsing and staging tool and service. Our process is simple: Analyze representative sample data from the users PLM environment. Use the sample export to configure the new environment. Parse and read the data from the exported data packages and then load the data into the PLM environment. Test and validate the accuracy of the migration process. When we have received the complete production extract, we run the full migration process.
The following steps are performed when migrating data from one source to another: extract data from the source system based on customer criteria, verify record counts and data content, perform/validate mapping of source data to target location, stage data into required data tables (item, rev, change, bom, etc.), run data through the ‘validation’ process, which checks for length, list values, correct subclasses, etc. – clean up any issues in either source or target system, load the data, review the load logs, commit the data load.
Although a data migration project may be unique, the desired result and the interim steps are always the same. We break the data migration down to project gateways, follow standardized procedures, utilize cutting-edge technology, and employ our own data loading and transformation utilities. Domain Systems’ data migration center offers an efficient, measurable, and accountable approach to completing the task.
Domain Systems has been providing data migration services for over 25 years. We have developed a unique methodology that ensures the success of your data migration project. Our team of experts will work with you to understand your specific needs and develop a customized solution that meets your business objectives. We offer a comprehensive range of services, including data extraction, data cleansing, data mapping, data loading, and data validation. Our proven methodology ensures that your data migration project is completed on time and within budget.
Part of these best practices involve simplifying the complexity of PLM for a smoother migration process.
Situations may arise, especially if there are multiple data sources, where legacy exceptions create difficulty in creating a 100% conforming format for migration. Domain Systems is available at the Customer’s request to assist the Customer with appropriate formatting (e.g. with cleaning and scrubbing) of legacy data to fit the target system such as required format and/or values, etc.
Domain Systems’ customers are successful at data migration because we apply strict methods and controls. We use a time-proven methodology on all data migration projects. Although a data migration project may be unique, the desired result and the interim steps are always the same. We break the data migration down to project gateways, follow standardized procedures, utilize cutting-edge technology, and employ many of our own data loading and transformation utilities. Domain Systems’ data migration center offers an efficient, measurable, and accountable approach to completing the task.
Data validation and cleanup is a critical part of our migration plan. We understand that legacy data can contain issues or inaccuracies, which is why we perform validation checks on the first-pass data extract to locate and fix these problems. Furthermore, we ensure quality transformation by extracting data from the source system and transforming it into a format that the target system can import and understand. This transformation process includes performing the defined data mappings and executing any underlying business logic functions that may be necessary for populating more complex data structures.
If you’re looking for expert guidance on PLM data migration, don’t hesitate to reach out to us at Domain Systems. We’re here to help you navigate the complexities of PLM and data migration.
We are a premier PLM (Product Lifecycle Management) solution provider. With decades of industry experience, we offer a spectrum of services, from consulting to system implementations and support. Our dedicated team collaborates with clients to understand their corporate goals, identify challenges, and devise strategic solutions to ensure success in the evolving digital landscape.
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