André Guldi

Data migration to Cloud PLM systems

Challenges and best practices for successful data migration

More and more companies are adopting cloud-based PLM systems to streamline their product development processes. Whether they are already using an on-premises PLM system and want to switch to a cloud solution or implementing a Cloud PLM system for the first time, one of the biggest challenges is the smooth and secure data migration.
How can this data be reliably transferred to the new system? In this blog post, we examine the challenges and best practices for successful data migration to Cloud PLM systems and offer tips on ensuring a smooth transition without data loss.

What challenges arise during data migration to Cloud PLM systems?

Migrating data to Cloud PLM systems, obstacles can present hurdles that complicate and delay the entire process:

Data quality and consistency
Legacy data is often incomplete or inconsistent. Missing attributes, invalid values, or duplicate records can hinder the migration process. Particularly with CAD models, missing files or broken references may prevent models from being imported completely
Data scope and complexity
Depending on the scope and complexity of the data being transferred, the migration process can be very time-consuming. Large datasets, such as entire version histories of CAD data or multi-level BOMs, require significant computing resources and can slow down the migration.
Structural differences between systems
Data structures in the new Cloud PLM system may differ from those in your legacy system. Attributes, data fields, or relationships between records may be organized differently, requiring data transformation or restructuring before import.
Technical challenges
Migrating data to a Cloud system brings specific technical issues. For example, along with ensuring file format compatibility, sufficient network bandwidth and data transfer rates must be guaranteed.
Security and compliance requirements
Strict security and compliance regulations must be followed when transferring sensitive data to the Cloud. Data must be encrypted during transport and storage, and data protection laws such as GDPR must be adhered to.

What key questions should you address before data migration?

Data migration is often underestimated, although it is one of the most critical tasks before a new PLM system goes live. You should address several key questions early to import your legacy data successfully.

First, determine which data objects will be transferred to the new system: Are you migrating CAD assemblies, parts and BOMs, office documents, or projects? It’s also essential to define the scope of the data: Do you want to migrate data from a specific project, a product, a specific company location, or the entire data archive?

You should also decide how much historical data you want to migrate. Do you want to transfer only the latest version or all versions, including the complete audit trail and engineering changes? These aspects are crucial as they influence the scope and complexity of the migration.

You should also carefully examine the content of the data itself. Consider whether all attribute values and CAD parameters are needed or if it’s sufficient to import only some of them. This is important to define which data should be stored in which objects and attributes in the target PLM system.

What makes data transfer with CIM Database Cloud so simple?

User-friendly import tools
The cloud-based PLM system CIM Database Cloud offers powerful, easy-to-use import tools specifically designed to simplify the migration process. They allow you a quick and efficient import of configuration data such as field selection values (e.g., dropdown fields), as well as PLM data such as CAD documents, parts, BOMs, office documents, projects, and requirement specifications.
Support for various file formats
CIM Database Cloud supports a wide range of file formats and data sources, making it easy to import different data objects. These include Excel files, CAD formats, and the ReqIF format for requirement specifications.
Automated validation processes
CIM Database Cloud includes built-in validation mechanisms that help identify and correct potential errors during the import process. These functions automatically check whether the data is complete and consistent during import, contributing to high data quality.
Iterative Migration Approach
The platform supports an iterative migration approach, allowing you to import and test data step by step. This helps identify and resolve potential issues early on, without affecting the migration process. This approach reduces the risk of errors and accelerates data migration.
Comprehensive Documentation and Support
Alongside the migration process, CIM Database Cloud offers extensive documentation and tutorials. These contain clear instructions and examples on how to import and configure different data types. Additionally, customer success managers are available to assist if needed.

Conclusion

Data migration to cloud-based PLM systems is often fraught with many challenges. Successful data migration, therefore, requires careful planning, considering aspects such as data quality, scope, structural differences, and security requirements.
CIM Database Cloud enables you to efficiently migrate your PLM data and make your product development processes future-proof . With user-friendly import tools, support for various data formats, automated validation processes, and comprehensive documentation, companies can ensure the seamless and secure integration of their existing data. An iterative migration approach, combined with extensive preparation, minimizes risks and guarantees a smooth transition to the new system.

Five project management issues that software must solve in the industry

What can software accomplish in engineering projects? What typical project management problems do digital tools have to solve in industry? Which functions are required for this? And how can small and medium-sized companies manage digitalization in this area with relatively few resources?

Project management software becomes essential

The requirements for product development are continuously increasing. This is driven by the growing share of electronics and software components, the high number of product variants, as well as new laws and compliance regulations. All of this inevitably impacts the complexity of engineering projects. Therefore, project management issues, particularly those affecting small and medium-sized enterprises (SMEs), can no longer be resolved without digital support.

Which problems must software solve in engineering projects?

Digital tools for project management are abundant: from Excel spreadsheets and SharePoint to dedicated software solutions. However, SMEs in the industry often encounter limitations with these tools. This becomes apparent when examining five common problems that suppliers face in project management:

1. Lack of transparency

It is crucial for the success of industrial projects that everyone involved has access to up-to-date project and product data at any time. Such a data foundation requires software that can be connected to all data sources in the development organization (ERP, CAD, CAx, etc.) via interfaces. Project management tools without integrated data management cannot achieve this.

This leads to various problems. Project participants often work with isolated solutions that are used only in specific departments, locations, or companies. The resulting lack of interfaces forces team members to exchange information manually, for example, through emails or SharePoint.

This approach generates a lot of work. Data must be continually updated, which distracts employees from more important tasks. Additionally, they need to regularly exchange information about the current status of the data. It is also common for data to get lost among the multitude of emails so that it has to be requested manually.

All of this creates delays that negatively impact project progress, costs, productivity, and customer satisfaction.

2. Significant risk of manual errors

If project teams do not have access to a consistent data foundation, the risk of misunderstandings during data exchange increases. When product and project data have to be entered manually into documents or spreadsheets, it is only a matter of time before transfer errors occur.

In the industry, such errors can have serious consequences. For instance, a transposition error made by an engineer can affect ordering processes in procurement. Similarly, the design team might develop their designs based on incorrect or unclear product data.

Such mistakes are not just annoying. They jeopardize the success of the project, drive up project costs, and damage the company’s reputation with customers and partners.

3. Limited flexibility for spontaneous changes

In development projects, it is not uncommon for customers to spontaneously change the requirements for a product. This often creates chaos, especially in small and medium-sized companies. To implement these changes, close coordination with the customer’s interdisciplinary teams is essential. Furthermore, suppliers must optimally time their own supply chains and implement changes as quickly as possible.

Both skills require flexible processes and clean data handling. Project managers must be able to plan and implement necessary processes at short notice, even after the project has started. In addition, data statuses must be precisely documented and reconciled between the departments and companies involved.

Otherwise, changes become expensive and time-consuming. For example, today only a few suppliers still use prototype tools. Instead, due to tight project timelines, they immediately move to steel and iron. This approach is faster but creates significant difficulties in the event of changes, which could be avoided through greater flexibility.

4. Experience-based knowledge is not used systematically

Insights gained from a customer project are rarely incorporated into the planning and execution of subsequent orders. This often impacts costs. For example, factors that caused delays in working with customers or partners are not considered when preparing the next quote. As a result, expensive mistakes or misunderstandings frequently occur.

When companies do not systematically document insights and lessons learned – which is not possible with every project management tool – the dependency on individual employees increases. For instance, during the COVID-19 pandemic, many experts left their companies due to poor order volumes. This loss of knowledge is particularly difficult for smaller businesses to compensate for in times of skilled labor shortages. In many companies, it still has an impact today.

5. New requirements due to laws and regulations

New laws and regulations bring numerous additional requirements for traditional industries. Many of them are aimed primarily at large companies. These, however, have to pass on specific requirements to their suppliers and partners.

Processes that companies have successfully used for decades no longer work under these circumstances. At the same time, suppliers lack the know-how and resources to adapt to the new conditions. For instance, customers are increasingly demanding that suppliers provide the carbon footprint of the parts they produce. This requires suitable processes and IT systems that companies must integrate alongside their day-to-day operations.

Laws and compliance requirements will continue to change the industry in the coming years. Those who ignore this trend risk losing their customers and, in the worst case, even face legal consequences.

What if companies ignore common mistakes in project management?

Project management software that is designed to meet the requirements of the industry must be able to solve these problems. If it doesn’t, project planning, management, and controlling will suffer, as will collaboration between project teams and external stakeholders. The results are

financial losses,
waste of resources,
delays,
unsatisfied customers,
loss of reputation,
frustrated employees,
internal tensions within the project team, and (in worst-case scenarios)
legal consequences.

Outlook

Many of the problems described in this article will become more pressing in the next few years. This is due to new laws and compliance requirements, but also the increasing use of new technologies. To address these issues, effective communication, efficient data exchange, integration of project and product data, and a high degree of standardization are essential. For this, you need software that not only offers project management functions but also includes PLM (Product Lifecycle Management) capabilities.

Our software CIM Database Cloud is such a solution. It is a powerful PLM tool that helps companies with product development as well as project management. The project management functions enable you to link schedules, tasks, and documents and support project managers in completing projects on time and budget. The cloud solution also makes it easier to connect globally distributed locations and implement laws and regulations.

How will the Data Act affect the industry?

Successful digital transformation requires access to data and its intelligent use. The EU has therefore defined a regulation that is intended to strengthen the European data market: the Data Act. Companies from traditional industries must adapt to it as soon as possible.

What is the Data Act?

The “Regulation on harmonised rules on fair access to and use of data” (Data Act) is a directive of the European Union that defines regulations regarding data access and use. It aims to create a fair, transparent framework for the exchange and use of data within the EU, thereby promoting innovation and increasing the competitiveness of European companies on the global data market.

The Data Act is a key component of the EU’s digital strategy. It was approved by the European Council on November 27, 2023 and came into force on January 11, 2024. Following a 20-month transition period, it is to be converted into directly applicable EU-wide law from September 12, 2025.

What is the motivation?

Data is a key resource in the digital economy. However, due to a lack of guidelines, legal requirements, and standards, a large part of the data generated remains unused, especially in industry.

Furthermore, we are currently observing a strong imbalance on the market: data is mostly owned by a small group of large companies. Compared to SMEs and start-ups, this gives them a considerable competitive advantage, which is reflected, for example, in one-sided contracts regarding data access and use.

To counteract this, the EU has developed the Data Act. It aims to democratize the market and create a balanced, fair data ecosystem. To this end, the EU has defined a legal framework ensuring that users of networked products or connected services can promptly access the generated data.

The objectives of the Data Act in a nutshell:

Clear rules for the use and exchange of data
Transparency and fairness within the data market
Protection of personal data
Secure data processing
Promotion of data-driven innovations
Increased competitiveness of EU companies

Who is affected by the Data Act?

The Data Act addresses companies, organizations, and individuals who

bring connected products to the market,
offer connected services,
as a data owner, share generated data with third parties,
receive data from data owners,
as a public institution, request data owners to share data, or
offer data processing services.

Persons who participate in data rooms and providers of applications that include smart contracts are also affected. Persons whose trade, business, or profession involves the implementation of smart contracts for others in connection with the execution of an agreement must also comply with the Data Act.

Which tasks result from the Data Act?

The Data Act imposes numerous new obligations on the industry. These include:

Making data accessible: Providers must ensure that users of connected devices or connected services have access to the data they generate.

Ensuring portability: The Data Act demands mechanisms that enable users to easily and securely transfer their data to third parties. This includes the development of standards and interfaces for data exchange.

Ensuring transparency and fairness: Companies must be transparent about what data they collect, how they use it, and who has access to it.

Ensuring data protection: The processing and disclosure of data must comply with applicable data protection laws (e.g., the GDPR).

Enabling cooperation with authorities: In many cases, it is necessary to pass on data to public institutions. This requires clear processes and responsibilities.

Data Act vs. Data Governance Act

The Data Act is not the only pillar of the European data strategy. It also includes the Data Governance Act (DGA), an existing regulation that defines processes and structures for the exchange of data between individuals, companies, and public institutions. In contrast, the Data Act focuses more on promoting the digital economy. It regulates which players are allowed to use the generated data under which conditions.

What are the consequences of violating the Data Act?

Unfortunately, it is not yet possible to predict how these aspects will be structured in detail. The EU regulation has not yet been transposed into German law. It therefore remains to be seen what obligations will arise in Germany and which supervisory authorities will oversee implementation.

However, one thing is clear: violations of the Data Act will result in fines, similar to the GDPR. There is also a risk that companies will be sued for damages by other market players if they fail to meet the requirements. Furthermore, it is possible that products and services that do not comply with the Data Act may no longer be sold in the EU.

Does the Data Act only create new duties?

The EU regulation does not only entail obligations. It opens up many new opportunities for SMEs in particular. If data is available to all market players in interoperable formats, this facilitates the implementation of innovative, data-based services, such as predictive maintenance.

This is precisely what the democratization of the data market aims to achieve. It gives companies more control over the way they handle their data and creates rules that facilitate data transfer. Both data owners and users will benefit from this.

Processes that are complex and time-consuming today will be accelerated. For example, the regulation provides clear rules for contract management. Cloud or edge providers, for instance, must contractually and technologically ensure that customers can transfer their data as easily as possible when they switch systems.

The industry will also benefit from increasing competition. For example, machine manufacturers who want to enable their products for the Internet of Things can currently only turn to a few providers for this purpose. The Data Act opens up this restricted circle. This not only increases the quality of products and services but also leads to lower prices.

According to a representative survey by the digital association Bitkom, Germany’s economy is currently divided on the Data Act. 49 percent of the 603 companies surveyed across all economic sectors see the new EU regulation as an opportunity for their business. On the other hand, 40 percent of respondents consider the Data Act to be a risk.

What is the best approach for companies?

Companies dealing with the Data Act quickly come up against complex issues: How do they ensure that the data interfaces of their machines, systems, and products are accessible to third parties? What impact does the sharing of data have on their business model? What opportunities does this present (e.g., new services and offers)?

Many of these questions are currently still unclear, making it difficult to prepare for the EU regulation. However, it is advisable to put the topic on the strategic agenda and seek an exchange with associations and other companies. This dialog helps assess the impact of the Data Act on your business.

Summary

With the Data Act, the EU wants to equip the European data market for international competition. The regulation promotes a secure, efficient flow of data and creates a framework that facilitates data exchange and use. This results in new business obligations, but also fairer market conditions.

How the Data Act will be implemented in Germany remains to be seen. Manufacturing companies should nevertheless get to grips with the contents as soon as possible. It is a complex set of rules that influences topics ranging from technological infrastructure to processes and contract design. Companies affected must adequately prepare themselves.

Further information

Handling data is becoming increasingly important for a company’s success. A reliable security architecture is essential, especially for cloud users. In our guide “IT security for companies”, you can read about the requirements for this and the factors you should consider when selecting software providers.