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Effective Data Migration Strategies to PLM system

Today, most supply chains are global and complex. Navigating through a company’s supply chain is no mean task. Managing and leveraging it for business growth is even more difficult. Therefore, companies use product lifecycle management (PLM) to keep up with the ever-evolving business dynamics.  

Product Lifecycle Management (PLM) refers to the strategic process of managing a product’s life cycle from start to end. It includes the functions of initial product ideation, development, service, and retirement within its scope.  

PLM software serves as a solution for managing all things related to a product beyond its design. It can include documents, data from items or parts, requirements, products, quality workflows, and engineering change orders.  

It has become vital for businesses to have PLM in today’s increasingly globalized world. And that brings us to PLM Data Migration strategies.  

What is PLM Data migration strategies, and how does it take place?


What is PLM Data Migration?  

It refers to the process whereby product lifecycle data is transferred either to an existing PLM system or a new oracle agile PLM system. It moves data from one (source/legacy) system to another (target/destination) system.   

The whole idea of data migration is based on the concept of ETL. It stands for Extract-Transform-Load. It divides the migration into iterations, each consisting of a phase (extraction, transformation, loading).  

Extract – the process of extracting data from the source system(s) onto the staging area.  

Transform – data transformation is applied to align the source data model to the existing one. This is also where data modification takes place.  

Load – having the transformed data loaded to the target system.

But you might wonder, why execute data migrations at all?  

The reasons for data migration can be multiple; it can take place due to the following:  

  • System end of life  
  • Current system upgrade  
  • Consolidation of data   
  • Ingesting new data into an existing system due to mergers/acquisitions  
  • Business splits or demergers  
  • Merging multiple datasets into one source-of-truth system


What kind of data is migrated?  

PLM data migration can include the following:  

  • Bills-of-materials (BOMs)  
  • CAD documents  
  • Effectivities and configurations  
  • Classification  
  • Change data  


Strategies for PLM Data Migration   

Employing the right strategy for data migration is essential. Moving large volumes of data can be very tedious in the absence of a viable data migration strategy. It costs both time and money.  

Moreover, a proper migration strategy means the data transfer is completed without hiccups. It also implies that your engineers can use the transferred data from the very first day.  

So how can you ensure smooth data migration strategies?   

By defining the best possible data migration strategy, one that aligns with your business goals and toolsets. This entails asking the right questions and answering them accordingly.   

Here is a set of questions – categorized in terms of different points of view – that can help you devise a viable data migration strategy.

Systems Architecture View:  

  • Are you going to replace your current agile PLM system or re-align it?  
  • Are you planning to set up a new system or migrate to an existing one?  
  • Will kind of interfaces to other systems will you require?   

Processual View 

  • Which processes will you implement?  
  • Are there any cross-system processes that you will implement?  
  • What kind of data will you share between the new and old systems?  

Organizational View 

  • What is your timeframe for completing the migration?  
  • Does your company have the right resources to undertake the migration in the set timeframe?  
  • Do you wish to do it in-house, or will you outsource the migration?  
  • What is your budget for the migration?  

Data view 

  • What sort of data will you provide?  
  • What volume of data will you deliver?  
  • How good are your metadata and CAD integration data?  
  • Will you migrate the whole history?   
  • What is the quality of your historical data?  
  • How will you reshape old data to fit new processes?  


Approaches to Data Migration  

The Big Bang Approach  

The Big Bang approach aims to migrate all data from one system to another in one go. It is also called bulk migration or one-time migration. This is the most common approach to data migration and is employed when the old system is completely replaced with a new one.  

One major drawback of this approach is the “blackout” period, a term used to describe the time when the employees cannot access data. This blackout period lasts only a few days.   

But organizations plan their big bang data migrations over the weekend to mitigate the blackout period’s impact. However, proper training of employees is necessary before a one-time migration takes place. This ensures that the employees are well-acquainted with the new system so as to keep the organization from becoming paralyzed.   

There are two sub-approaches within the big bang approach. These are:  

  • Big Bang Data-centric Approach – This approach migrates all data to the new system in one go.   
  • Big Bang User-centric Approach – This is a rather complex approach to data migration. Instead of moving both data and users to the new system in one go, the user-centric approach moves all users to the new system at one point in time. Data is migrated at the very end when the users actually start using the new system.  

The key benefit of this approach is a reduced blackout period. However, it requires extra attention to detail to keep the data from being corrupted.   

Incremental/Phased Data Migration Approach  

Think of an organization with multiple departments, each utilizing a data set independently. Phased data migration strategies moves each department to the target system one at a time.  

The incremental data migration approach helps complex organizations switch from one PLM system to another. It proceeds at your choice of pace and does not take place in one go.   

Users may continue using the old system until they finish an existing project. Or they can move to the target system to commence working on a new project. Data ownership is transferred, and information is synced from the old system to the new system.   

Reasons to employ the incremental data migration approach 

Some of the reasons why the incremental data migration approach will suit you better are:  

  • A large userbase and database on the legacy system that cannot be moved to the new system in one go.  
  • Multiple customer sites need to be migrated to the new system  
  • Each site can be migrated separately  

You must first determine how the source system data and users will be allocated to migration chunks if you wish to employ this data migration approach. Further, you need to ascertain the order in which the migration will be executed.


Pros and Cons for Data Migration

The end goal is the same: migrate all data from the source system to the target system. The primary benefit of this automation engine approach is that it allows you time to train your employees. Moreover, makes the migration process more manageable by breaking it down into chunks.   

However, the drawback is that it requires more planning and effort than a one-time migration. Also, it can lead to migration chunk overlaps or discrepancies. Such inconveniences can be remedied by defining and implementing a process to deal with them.   

You can achieve this by ensuring that all objects have a clearly recognized “master” (source or target system) at any point in time. It also entails ensuring that the migration team and all the users are aware of these object-master relationships and do not make any changes.


The Coexistence Data Migration Approach  

Of all the data migration approaches, the coexistence data migration approach is the most difficult one to implement. As the name suggests, this approach requires both source and target systems to coexist and to remain synchronized until the migration completes.   

This allows users to use either system at any point in time during the migration process. It also requires the establishment of a bi-directional communication interface between the two systems.   

This approach mainly seeks to mitigate the impact of migration. It allows for easier identification and correction of faults and a smoother migration overall. However, its downside is that it is tricky to implement owing to the constant need to keep both systems synchronized.   

Also, if your source data is bad, the target system will also reflect it. We call this the ‘garbage in, garbage out’ (GIGO) problem.


Ingredients of a successful PLM Data Migration  

What goes into making a PLM data migration successful? We have outlined just the ingredients you would need to make your data migration strategies successful.  

  • Defining Source and Target Data Models  

Data migration from one system to another usually means that you would be moving to a new data model with document portal. A new data model is unlikely to align with your source or legacy data model. This means that you will have to put in a significant mapping effort to ensure that each piece of information in the source system is aligned with the target system.  

The key is to take it as an up-front exercise aimed at locating data model discrepancies and overcoming the same. This will save your whole migration from becoming jeopardized.   

  • Role of Subject Matter Experts  

Data issues almost always accompany data migrations. These issues are resolved with help from subject matter experts who can identify what is essential, what is needed, what is incorrect, and what is irrelevant. Their help is crucial in executing a migration smoothly.  

  • Communication  

As in other aspects of life, communication is pivotal in driving home a successful data migration strategy. The migration team, business executives, and subject matter experts need to communicate with each other for a seamless migration to take place.  

Goals and timelines should be prioritized and planned efficiently. But that’s not it; you also need to appropriately monitor and manage the timelines to complete the process with the least number of hiccups.   

  • An Experienced Migration Team  

Who would want inexperienced interns to play with the company’s data? Data migration is a serious business and thus requires an experienced team to manage it.   

You may choose to do the migration in-house if your team has the required skill set. But if it doesn’t, we recommend that you outsource the process to a system integrator or service provider.   

It will cost you a certain amount, but it would be worth it since jeopardizing the data you’ve spent years building is not a good idea. A service provider will either guide you through the migration process, supplement your team, or execute the whole migration by itself.   

The choice, of course, is yours to make.

If you wish to know more about data migration to PLM or require assistance in executing your migration strategy, please feel free to contact us at info@xavor.com 


Muhammad Abbas

Muhammad Abbas is an anthropologist at heart and a Marketing Specialist by profession. He has worked with leading marketing agencies over the years. While he considers himself a history buff, his interests also extend to topics like politics, economics, social justice, climate change, and tech.