Agile PLM Migration

Every business will eventually migrate their data into a new solution 

Transferring data into a new system presents a list of potential challenges, which can ultimately have devistating repercussions on your organization.

Luckily, a growing number of IT systems implementors and data migration services follow a similar methodology, which mitigates virtually all of the risk involved.  As with other projects, the process of migrating data can be broken down into a series of tasks, metrics, and procedures that reduce costs and time to completion, yet virtually eliminate error.

Database schemas are different, characters in field text can become “illegal,” and unavoidably some existing data and relationships will simply be erroneous. 

“Domain Systems provided a professional, friendly and effective service to our company. They used their experience to successfully help us through the installation, configuration and data extraction phases of the Agile introduction, providing useful advice at all times.”

William Atwood

IT Supervisor, (Medical Device Manufacturer)

3 best practices that will ensure you have a successful migration:

Comprehensive Mapping

A geographical  map is essential for traversing unknown lands

Every data field that is going to be migrated from the source system to the target system must be defined and examined to ensure  compliance with field lengths, data types, system rules, and other possible issues. 

It is critical to understand where information is going as well as if there are any known and avoidable obstacles in the way of a successful migration.

Data Validation

“A good craftsman measures twice and cuts once”

The data in a legacy system often contains problems or can be unknowingly incorrect due to any number of reasons. Any validation rule that can be utilized to locate and fix these problems is be performed on the first-pass data extract. Whereas the source system may ignore the discrepancies with items like duplicate reference designators on a Bill of Material, the target system typically will have tighter business rules. 

Quality Transformation

Language gaps are bridged by the interpreter

Data is extracted from the source system and transformed—or translated—into a normalized format that the target system can import and understand.

This transformation will not only perform the defined data mappings, but will also execute any underlying business logic functions that may be essential to populating more complex data structures.

Learn about some of our more popular Process Extensions:





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