PLM Data Migration Strategies

Introduction

PLM Data migration is a systematic process of transforming data from one PLM system to the other. Each migration project is different as the solution is driven by the complex requirements gathered to the granular level. With such volatilities in place, data migration projects turn out to be disasters if not planned and architected carefully.

PLM data migration strategies play a vital role in averting disasters.

– CAD Data:

CAD data constitutes the digital designs, including 2D drawings, 3D models, and simulations. Accurate migration of CAD data is vital to preserve the design intent and facilitate continued product development in the new PLM system.

– Bill of Materials (BOM):

The BOM captures the hierarchical structure of a product, detailing all components, sub-assemblies, and raw materials needed for manufacturing. Accurately migrating the BOM data ensures efficient collaboration and streamlined production processes.

– Engineering Change Order (ECO):

ECOs track proposed and approved changes to products, BOMs, or related documents. Migrating ECO data with precision is essential for maintaining product integrity and documenting changes for future reference.

– Product Specifications & Attributes:

This data includes detailed product specifications, defining its features and characteristics. Accurate migration of this data ensures that product information remains consistent and easily accessible in the new PLM system.

– Manufacturing Process & Instructions:

These instructions outline the step-by-step procedures for product manufacturing. Migrating manufacturing process data ensures that production operations continue smoothly without disruptions.

– Supplier & Vendor Data:

Supplier and vendor information, such as contact details and performance metrics, must be migrated accurately to maintain supply chain continuity, and facilitate vendor management.

– Quality Control & Vendor Data:

Quality control data encompasses inspection reports, testing results, and quality standards. Migrating this data guarantees that product quality is upheld in the new PLM system.

– Regulatory & Compliance Data:

Data related to industry regulations, certifications, and compliance standards must be migrated to ensure adherence to legal requirements and industry best practices.

– Document & File Attachments:

PLM systems often store various supporting documents like product manuals, certifications, and reports. Migrating these attachments with their associations to relevant products is crucial for retaining comprehensive product context.

– Historical Product & Project Data:

Preserving historical data, including previous product versions and project details, allows teams to refer to past information, making data migration inclusive of the entire organizational knowledge base.

– User & Access Permissions:

Migrating user data and access permissions ensures that the right individuals have appropriate privileges in the new PLM system, maintaining data security and governance.

– Relationships & Associations:

These data include relationships between different PLM entities, such as associating CAD designs with specific products or linking BOMs to their corresponding projects. Migrating relationships accurately is vital for maintaining data integrity and interconnectedness.

3. Data Migration Process

– Analyse

The Data Migration Process begins with analyzing the Source System. This entails gaining a comprehensive understanding of the source system’s intricacies and reviewing any existing documentation. This step forms the bedrock for a smooth migration, ensuring we’re well-versed with the data’s structure and essence.

– Data Extract

Data Extraction employs suitable methods and tools to extract data from the source system. This might involve diving into databases, files, or applications to retrieve the relevant data that’s earmarked for migration.

– Cleanup & Transform

Data cleanup applies business rules and data transformation processes, shaping the data into a consistent, accurate format that aligns with the standards expected by the target system.

– Validation

Preload reports come into play, allowing us to validate the data’s integrity before it finds its new home. These reports unveil any potential issues or discrepancies that necessitate our attention before the actual migration.

– Loading the data

In Loading the Actual Data precision and timing are key. We carefully execute the data loading process, ensuring the clean and validated data finds its rightful place in the target system with optimal business downtime.

– Reconcile

Reconciliation enters the scene. Post-load reports and post-load errors are meticulously reconciled. Any discrepancies are addressed, ensuring that the migrated data stands true and accurate in its new environment. In essence, this process orchestrates the seamless journey of your data, safeguarding its integrity and usability throughout.

4. Data Migration Strategy

PLM Data Migration Strategies refer to a set of deliberate approaches designed to smoothly transition data from one system to another within a Product Lifecycle Management framework. These strategies are carefully crafted plans that ensure data integrity, minimize disruptions, and optimize the use of the new PLM system.

In essence, these strategies act as the guiding principles that shape how we migrate data – defining the sequence, methodology, and timing to achieve a successful migration.

Some of the crucial prerequisites for a successful PLM data migration strategy are:

– Big bang data-centric migration strategy is one of the simplest approaches where all the data in the scope of migration is migrated to the target system at once. This approach allows users to be onboarded either before or during the production migration. Big Bang user-centric migration is slightly different from the data-centric approach. In this case, the first cut of data is migrated to the target system while engineers are still working in the source system. Only the delta data created between initial cloning and the go-live is migrated to the target system during the go-live period. Users are onboarded in the target system during the production go-live. This approach has an edge over the data-centric approach as this induces minimal migration downtime.

– Phased Migration with a Manual Synchronization Strategy or Approach is a strategy where the Data is migrated in chunks to avoid huge downtime and other risks related to migrating moderately huge data. Only the required data is synched between systems using a manual approach while the migration toolchain is built to migrate only from source to target.

– Phased Migration with a bulk synchronization Strategy or Approach is the most complicated and sophisticated strategy used for huge data migration. In this approach, the Data is migrated in chunks to the target system during production migration. The toolchain is designed to migrate any required data in either of the directions (reference only) at any point in time while maintaining the data co-existence.

6. Selection Criteria of Migration strategy

Major factors that drive the selection of migration strategy are:

– Big bang approach is suitable for small to Medium sized data. However, if the data is large then phased approach is preferred.

– Big bang migration requires less budget in comparison to phased migration, as efforts involved in enabling migration solution to sync the data back to source system and keeping the systems in sync are significant.

– Big bang migration project timeline is short as the entire bulk of data is migrated at once in the end whereas phased migration takes longer time to complete migration of data in chunks.

– Big Bang migration demands the involvement of all teams and resources at a time, while in phased migration approach, resources are engaged in phased manner.

– In big bang migration risk factor is high as moving entire data back to source system or aborting migration in the end for some unexpected or unforeseen factors is a significant setback. Whereas in phased- migration, migration starts with only partial data and the migration solution also includes the solution to revert data back to source system if difficulty arises.

7. Big Bang User Centric Migration

In this strategy, the entire data is migrated in one go while the business is still working in the source system, and only the data created or modified in the source system during bulk migration is migrated during blackout or go-live downtime.

Users are onboarded to the target system during production go live as the bulk of the data is migrated before go-live.

This strategy costs a marginal premium over the Big Bang Data Centric approach and is suitable for businesses that can’t afford huge downtime.

7.1 Big Bang Migration – User Centric Sample Timeline

– Development and Testing Stage

The actual data migration kicks off with the architecting solution and design and development of the same.​

Toolchain development takes more time to incorporate delta migration capability along with the ability to migrate bulk data before go live.

This strategy requires extensive testing as the toolchain is also built to handle delta data migration.

Pre-Production Migration and Testing

In the Pre-Prod Migration & Testing Phase, the entire data in scope is migrated along with the delta data created post-data priming.

This approach also includes the development of a mechanism to lock or make the data invisible to business users during the priming phase. This locking mechanism is also thoroughly tested and prepared for production migration.

Either user training alone is taken care of during this phase if users are targeted to be onboarded during production go-live, or both training and onboarding are done during this phase itself.

– Production Data Priming

In this strategy, Stock of the data is taken for migration before data priming and users are allowed to work in both source and target systems during this phase.

This is made possible by the mechanism that locks or makes the data invisible for business users in the target system until go live decision is made. This mechanism also gives the privilege to initiate the data priming in advance as the data stays invisible to business users until the lock is lifted.

With the majority of data migrated even before production go live, the success of the migration is realized even before the actual production migration thus making it substantially less risky.

– Production Migration

Priming contributes to almost 80% of the data being migrated, early priming gives sufficient time for fixing failures before production go live. Thus, minimizing the production downtime significantly.

– Post Migration Support

As the majority of the failures from the data priming phase are fixed before production go-live, the post-migration support team will be dealing with the failures from delta migration performed during production go live. So, a small support team is sufficient with this approach.

– Pros and Cons

Pros of this strategy: –

• The Big Bang user-centric approach offers several benefits that make it an appealing choice for PLM data migration.

• The user-centric migration approach excels in managing a marginally higher quantity of data migration, making it suitable for scenarios where a large volume of data needs to be transferred seamlessly.

• One of the biggest advantages of this approach is that it implies significantly less business downtime. This means that the impact on regular business operations is minimized, ensuring a smoother transition.

• Early priming in this approach supports the migration of more complex and less-quality data, providing flexibility in handling diverse data types and ensuring a comprehensive migration.

• Managing post-migration support for users becomes more streamlined and can be handled by a smaller team. This contributes to operational efficiency and ease of maintenance in the post-migration phase.

• The strategy is well-suited for digital transformation projects to some extent, offering adaptability to the evolving needs of modernization.

Cons of this strategy: –

• One of the biggest drawbacks of the user-centric migration approach is the longer project development and execution timeline. This may impact the overall project schedule and time-to-market.

• The approach demands a marginally higher budget, considering factors such as the need for specialized skills, tools, and resources to ensure a smooth user-centric migration.

• We must develop the toolchain to handle the delta data updated in the source system during the data priming phase, which requires more time. This could potentially extend the project timeline and increase resource allocation for tool development and validation.

In summary, the user-centric migration approach offers advantages in handling diverse data and minimizing business disruption, but it comes with trade-offs such as an extended timeline and higher budget requirements. Understanding the specific needs of the project and weighing these pros and cons can help in making informed decisions during the migration process.

8. Big Bang Data Centric Migration Strategy/Approach

Big Bang Data Centric Migration Approach involves migrating the entire data from the source system to the target system in a single decisive move.

In this case, users are either onboarded before bulk migration or during migration as it doesn’t matter much because the target system is locked until production go, no go decision is made.

However, it demands extensive preparation, covering data readiness, infrastructure readiness, and logistics planning.

It’s particularly well-suited for small organizations with less data to be migrated.

This is also the most economic migration strategy if planned, architected, and executed perfectly.​

But, given its comprehensive nature, a robust rollback plan is essential to be able to resume work in source system during unexpected challenges.

8.1 Big Bang Migration – Data Centric Sample Timeline

– Development and Testing Stage

The actual data migration kicks off with architecting solution, and design and development of the same.

Time and efforts involved in development and testing of migration solution for this approach are the least in comparison to the others. This is because the toolset and processes involved in migration are meant to migrate all the data in one go while inevitable leftover data is handled manually.

Moreover, because all data is migrated in one go, the entire toolchain is developed comprehensively and rigorously tested well, in advance of the pre-production migration. This meticulous preparation ensures that the migration tool chain and process are robust and reliable for production migration.

– Pre-Production and Testing stage

In Pre-Prod Migration & Testing Phase, the entire data in scope of migration comes into play. It’s a comprehensive assessment of the success rate and any potential issues before the actual production migration takes place. Think of it as a dress rehearsal, ensuring that everything is in order before the main show. Projects might have one or more of such rehearsals based on the complexity and requirement.

One of the key aspects for successful migration is the rigorous testing. A major chunk of business users are actively involved in this phase. They put the system through its paces, examining how it handles real-world scenarios and challenges.

As a result of this rigorous testing, any errors or issues that arise are promptly reported. This report helps in final round of error fixing that ensures the system is in its best possible shape for the production migration.

– Production Migration Stage

In the ‘Production Migration’ phase, both the source and target systems are temporarily blocked to ensure a smooth and interference-free migration. This strategy induces a significantly high blackout period in comparison to other approaches, as the data migration starts only after locking the users in source system.

The downtime is also called a blackout period as nobody else data migration team will have idea about things done.

A critical go, no go decision is awaiting in the end to determine the success, following which the normal operations are resumed in the new system.

– Post Migration Support

This strategy demands a big support team for post migration handholding, for a short duration until all the business users get acquainted with the new system.

Additionally, a robust rollback or mitigation plan remains a prerequisite. While we strive for a seamless migration, having a contingency plan in case of unexpected issues is a strategic safety net. The entire data and user base are involved in it thus rollback invites a tried and tested large-scale actions that are timebound.

In essence, ‘Post Migration Support’ is about having right people and plans in place to ensure smooth transition of both Go and No-go decisions.

– Pros and Cons

Pros of this strategy: –

• It is the most simplified approach technically as the solution only include migration of all the data at once with no scope for incremental data migration, thus it is suitable for small and less complex requirements.

• This simplified construct aids in making it the fastest of all the other approaches. Since this approach include the tool set designed to migrate data only once, it makes it the most economical too.

Cons of this strategy: –

• The highest go-live downtime that it induces where users are locked in both the systems until go, no go decision is made post migration.

• Adoptability of the entire user base to the new system will impact product development timeline so it also demands a reasonably huge team to support users post go live.

• With users waiting to resume work in target system, there’s substantial risk if not executed perfectly for any unforeseen reasons. Thus, this approach involves rigorous testing from all the departments using the migrated data.

• This approach is usually not suitable for the migration projects that are a part of digital transformation where the migration timeline is dependent on many other factors.

In summary, Big Bang Data Migration offers speed and simplicity but requires thorough planning and testing to address its challenges.

9. Phased Migration with Manual Synchronization Strategy/Approach

In this strategy, data is migrated in chunks or batches in various phases.

The data migration strategy followed for each of the phases could either be a big-bang data-centric or a big-bang user-centric. In essence, it is a strategy that is formed by stitching multiple big-bang approaches.

While the bulk of the data is migrated in phases, a small amount of data is migrated in either of the directions manually. This allows business users to continue working in both systems with a small amount of shared data migrated between systems.

This strategy costs a premium over Big Bang migration approaches as the migration solution not only includes the migration of data in phases but also bi-directionally.

It is suitable for medium to large-scale businesses that target gradual transformation to avoid business interruption and to have good control over the progress.

9.1 Phased Migration Manual Synchronization Sample Timeline

– Development and Testing Stage

During the development and testing stage, we focus on three key aspects. First, building the toolchain, which might take extra time for iterative data migration. Second, rigorous testing is needed for the manual synchronization process. Lastly, in the phased migration approach, we categorize data by features, to take care of complex data in the last phase.

– Pre-Production and Testing Stage

Before officially moving to production data migration, we go through a pre-production phase. During this phase, testing is a crucial step to make sure everything works smoothly.
Ensuring the source system is ready and compatible for bi-directional synchronization is also a part of this preparation.

Bulk migration is moving a lot of data at once and manual synchronization where human involvement is needed.

The goal is to make sure these two methods work seamlessly together during this phase.

– Production Data Priming Stage

Here, Data is meticulously prepared, and all the data of a particular phase to the target system, while people continue to use both the source and target systems. However, manually synced data adds a bit of complexity both in terms of execution and tracking.

– Production Migration

In the last step of moving things from the source system to the target system, we only transfer the stuff that’s new or changed recently. Doing this makes the whole process faster and easier. Each time things shift for a new phase, it gets quicker and simpler because we learn from each time, we’ve done it before. So, every step teaches us how to make the next one go more smoothly.

– Post Migration Support

Post-migration support refers to the assistance provided after transferring data to a target system. This support team works closely with the On-demand synchronization team, ensuring a smooth transition. They play a role like the big bang user-centric team but with a focus on a more extended duration of help, offering ongoing support beyond the initial migration phase. This ensures that any issues or questions arising after the migration can be addressed promptly and effectively.

– On-Demand Synchronization

Occasionally, we must manually update data that has been changed or newly created. This is done to make sure that shared or contextual information is up to date in both systems. This allows users to work in both systems with all the required carryover data efficiently. This arrangement is temporary and comes into effect after phase 1 and ends with the migration of the last phase data.

– Pros and Cons

Pros of this strategy: –

• Phased approach is well suited for medium to large-scale Migration projects.

• It is effective for migrating complex data, making it suitable for projects with intricate data structures.

• Migrating a small amount of data in early phases allows for better understanding and adaptation in later stages of the project.

• The scope of initial phases can be adjusted and accommodated in later phases, providing flexibility in project planning.

• This approach is less risky, making it ideal for projects with numerous dependencies and transformations.

• Phased migration with manual synchronization is well-suited for digital transformation projects.

Cons of this strategy: –

• Comparatively, this approach can be reasonably expensive when compared to Big Bang approaches.

• The project execution timeline is longer to accommodate migration and planning in phases.

• It demands resources to be available for an extended period, impacting resource planning.

• Managing the combination of bulk and manual synchronization can be complex and requires careful coordination.

• Before production migration, a specific test source system is required to test bi-directional synchronization, adding up to the cost.

In essence, while Phased Migration with manual synchronization offers flexibility and risk mitigation, it comes with the trade-off of higher cost, an extended timeline, and resource demands. Careful consideration is needed based on project requirements and constraints.

10. Phased Migration with Bulk Synchronization Strategy/Approach

Phased migration with bulk synchronization is an advanced data migration strategy tailored for substantial enterprises dealing with extensive data volumes and intricate interconnections.

This approach uniquely facilitates the gradual transfer of projects, accommodating the seamless exchange of large data between them.

The effectiveness of this strategy is in its capable mechanism, guaranteeing the smooth synchronization of significant amounts of data between source and target systems. This capability enables users to operate in both systems concurrently with minimal disruptions over an extended duration.

The strategy adopts a big bang user-centric migration approach for each phase, prioritizing the end-users experience during the transition.

10.1 Phased Migration with Bulk Synchronization Sample Timeline

– Development and Testing Stage

During the creation and testing of the tool chain for achieving bulk bi-directional data synchronization, we enter a phase that demands time and attention. Developing a mechanism capable of effectively managing the simultaneous transfer of substantial data in both directions requires meticulous effort.

In this context, rigorous testing of the entire solution becomes crucial. The aim is to ensure the seamless operation of the bulk synchronization mechanism and its ability to handle the complexities of the migration process. While this testing approach is time-intensive, it is fundamental for the success of the implementation.

– Pre-Production and Testing Stage

In the pre-production migration and testing stage, we conduct a trial run before the actual data migration to ensure a smooth process. Specifically, for this type of migration, we need a test source system that allows us to practice synchronizing a large amount of data in both directions. This ensures that the mechanisms for bulk bi-directional synchronization work effectively. Moreover, the pre-production migration demands active participation from business users who are essential for testing the entire synchronization process from start to finish. Their involvement is extensive and critical in validating the end-to-end synchronization, ensuring that the migration aligns with business requirements and functions seamlessly.

– Production Data Priming Stage

Production data priming of this approach is the best as tool chain takes care of migration of mixture of complicated data in either of directions smartly. The process becomes more streamlined for later phases as of the most shared data is synchronized using the tool chain following on-demand migration requests from business users. With tool chain’s ability to manage data co-existence efficiently, data priming of later phases could be started anytime.

– Production Migration

In the production migration phase, the data that was modified or newly created during priming moves over seamlessly. This strategy is efficient because it keeps downtime to a minimum. We initiate the production migration only when we’ve confirmed that the priming went well and achieved the desired success rate. The focus is on ensuring a smooth and controlled transition of data.

– Post Migration Support

In the post-migration support stage, the team collaborates closely with the On-demand synchronization team. Typically, this process involves comprehensive migration validation checks, addressing most issues during the migration and fixing them right away. As a result, a compact team is sufficient for this phase due to the proactive handling of potential issues during the migration itself.

– Bulk Synchronization

In the bulk data synchronization stage, data that has been changed or newly generated and belongs to the data already migrated is synchronized daily between systems. This synchronization occurs either automatically or is managed by a team of migration engineers. Importantly, this process is carried out without requiring users to request synchronization, allowing them to concentrate on their work without interruption.

– Pros and Cons

Pros of this strategy: –

• Phased migration with bulk synchronization emerges as a favorable approach for large-scale and intricate data migration initiatives.

• This strategy facilitates efficient bulk data movement through a well-designed migration tool chain and minimizes downtime by ensuring data co-existence during the migration process.

• The phased approach evolves the tool chain across stages, simplifying later-stage data migration.

• It proves to be a low-risk option, particularly beneficial for projects with dependencies and transformations, making it well-suited for organizational digital transformation efforts.

Cons of this strategy: –

• It stands as the most expensive approach in terms of both development and maintenance.

• Prolonged resource involvement and the longest implementation time, in comparison to other migration strategies, add complexity that demands continuous expertise.

• The need for a dedicated test source system and extensive testing across all involved domains is necessary.

In conclusion, the phased migration with bulk synchronization strategy presents a comprehensive solution that is ideal for large-scale and complex data migrations. It offers advantages such as minimal downtime, low risk, and adaptability to dependencies.

However, it is also the most expensive approach, demanding prolonged resource involvement, and having a lengthier implementation timeline, necessitating careful consideration based on project priorities and organizational resources.

– Complexity

Talking about Complexity, starting with Big Bang data-centric migration is straightforward. But as you move to phased approaches, things get more complicated. You must sync data in parallel, making the whole project more intricate compared to the simpler big bang method.

– Risk

Considering Risk, going for Big Bang data-centric migration has the highest risk. The risk decreases as you move to Big Bang user-centric, then Phased migration with manual synchronization, and the least risk is in Phased migration with bulk synchronization. So, it’s about managing risk smartly.

– Down Time

Downtime is crucial too. Big Bang data-centric migration needs the most downtime. The other strategies, like Big Bang user-centric, Phased migration with manual or bulk synchronization, are designed to minimize this downtime, making them less disruptive options.

– Data quantity

Managing a large amount of data works well with phased migration and bulk synchronization. It’s like breaking down a big task into smaller, more manageable pieces.

– Infrastructure requirements

Finally, there’s a shift in Infrastructure Requirements as well. Big Bang migration provides greater flexibility with infrastructure, allowing for some compromises. On the other hand, Phased migration with bulk synchronization requires more infrastructure since it involves the constant movement of a large amount of data.

12. Conclusion

Four main strategies are compared based on factors like time, complexity, risks, and more.

– Big Bang Data-Centric

Big Bang Data-Centric migration is a quick and cost-effective way to transfer data. It doesn’t require a lot of skilled people or complex infrastructure. However, it comes with some downsides – it’s a bit risky, can cause significant downtime, and may not be ideal for handling large amounts of data.

– Big Bang User-Centric

Big Bang User-Centric approach breaks the move into two steps, that is data priming and delta data load. Although it takes more time, it is safer with less downtime. Yet, it’s a bit more complex, costs a bit more, and requires experienced individuals compared to Big Bang Data-Centric migration.

– Phased Migration with Manual Synchronization

Phased Migration with Manual Synchronization – this is a good choice if you have a lot of data and want to be cautious about risks and downtime. However, it takes more time, costs more, and demands skilled people and equipment compared to the Big Bang migration approach.

– Phased Migration with Bulk Synchronization

Phased Migration with Bulk Synchronization has the least downtime, minimal risk, and can handle the most data. Nevertheless, it’s more expensive, takes the most time, and requires a high level of skilled individuals and infrastructure.

Each method has its pros and cons. Choosing the one that fits the requirement is always challenging no matter what we do to understand the suitability and effectiveness. Migration projects are ever-learning and arduous ones even for the experts as there is always some or other new thing to learn almost every time. When choosing the best one for your needs, consider how quickly you need it, how much complexity you can handle, and how much risk or downtime you can tolerate, and most of all; involve the experts from the inception to lay a strong foundation and avert disasters in later phases.