We have been observing for years how companies in the German mid-market are increasingly confronted with the challenges of outdated custom software and rising maintenance costs. Data migration and the replacement of legacy systems are central topics in this regard. Anyone who fails to plan data migration correctly or overlooks important aspects risks data loss and lengthy operational interruptions. Our goal is to design the process in a structured way and provide practical recommendations so that data migration in legacy systems succeeds without complications. In this article, we share our experiences, show best practices, and explain why forward-looking planning is indispensable for successful modernization.

Why Data Migration is Critical for Success

Digitalization in mid-sized companies continues to grow. Flexibly scalable solutions, automated data analytics, or cloud services are no longer just "nice to have" but often already determine business success. This is precisely where it becomes clear that outdated technology quickly becomes a barrier. Whether due to a lack of compatibility with modern tools or persistent performance issues, a legacy system can limit innovation power and block urgently needed innovations.

In this context, data migration is much more than just a technical move from A to B. Rather, it is a strategic step to remain competitive in the long term. It affects all areas: from customer service through controlling to product development. According to a 2023 survey by OpenLegacy, 88% of companies are so severely restricted by outdated systems that they can no longer efficiently manage their operational processes [1]. From our perspective, this is a clear signal to tackle migration promptly — if it has not already happened.

In addition, a successful migration saves costs in the long term and reduces risks. Already in the first project phases, potential can be identified to reduce maintenance costs while at the same time minimizing security risks. Anyone who proceeds proactively here is laying solid foundations on which further digitalization initiatives can be built. This also means completely replacing legacy systems or modernizing them in a targeted manner, as we explain in more detail in the context of replacing legacy systems.

Understanding the Challenges of Old Systems

Before we go into best practices for migration in detail, we want to outline the particular challenges that outdated systems typically bring. Legacy software may have been in use for decades. It is often based on older programming languages or proprietary database systems that hardly receive any support in the current market. According to an analysis by Docusnap from December 2025, legacy systems are often deeply intertwined with business processes and thus make a quick switch more difficult [2].

This entanglement with established industry or sector-specific software means that every migration is tricky in many respects. As soon as we shut down or migrate an old system, this can inevitably lead to outages of important core processes. Therefore, careful planning — including outage and risk management — is indispensable. Only in this way do we avoid data loss or standstill in central areas such as production, sales, or customer service.

A further complication is that knowledge holders or developer teams who once built the system are often no longer in the company, which additionally complicates the maintenance of outdated technologies or the application of patches. The shortage of skilled workers for old technologies also plays a decisive role here. New IT professionals prefer to work with modern technologies, while maintaining old languages or databases is often considered unattractive. This personnel situation can slow down the migration process and drive up costs.

A further essential point is data security. Old databases and operating systems are no longer regularly updated. Known security gaps thus remain open and are potentially exploited by cybercriminals. Compliance regulations, such as those required for data protection or industry-specific regulations, can often be difficult to fulfill in legacy environments. According to Skillfield (2024), compliance with modern security and compliance standards is a core driver for switching to new platforms [3].

The Steps to a Successful Migration

To proceed with data migration in legacy systems as safely and efficiently as possible, we rely on a structured approach that usually comprises five essential steps. Each step requires careful planning and close coordination with stakeholders from IT, business units, and management.

1. Inventory and Goal Definition First, we conduct a comprehensive system and data audit. We capture all relevant data sources, process chains, and interfaces. We thus gain a clear picture of data volume, data quality, and critical dependencies. We also define concrete goals: which database technology will be used? How high are the performance requirements? How will future extensions be incorporated?

2. Choose a Migration Strategy Taking into account the project framework, technical conditions, and risk tolerance, we choose the appropriate strategy. Whether big bang vs. incremental migration or the option of a strangler fig pattern migration, our decision depends on how critical the existing system is for daily business operations and how quickly results are needed. With the Big Bang method, the entire system is moved on a single cutover date, whereas a step-by-step migration reduces the outage risk but ties up more resources over a longer period.

3. Data Preparation and Cleaning Migrating old data is also an opportunity to clean up outdated records, duplicates, or inconsistent entries. Such a "data check-up" includes deleting redundant or invalid entries as well as standardizing data types and formats. Thorough tests beforehand prevent inconsistencies and transformation errors. The rule here: the more accurate the data foundation, the faster future analyses and applications can be realized.

4. Technical Implementation and Test Phase Then comes the actual implementation. This includes setting up the target environment, developing ETL processes (Extract, Transform, Load), and validating the migration results. In pilot phases, we check partial data and test migrations for correctness and performance. These test runs are essential to uncover errors in advance and avoid expensive setbacks. A fallback concept including data backup for emergencies is mandatory here.

5. Rollout and Aftercare After the final data transfer and the system change have taken place, a phase of intensive control and optimization begins. Smooth live operation is the goal. We maintain close coordination with the business units to take in user feedback and quickly remedy problems. Training and continuous exchange help to firmly integrate the new system into everyday work. In this phase, we also rely on monitoring and reporting tools to detect possible performance bottlenecks early.

Best Practices for a Smooth Procedure

During the migration process, we have to manage a wide variety of factors simultaneously, from resource planning through stakeholder communication to technical details. In doing so, several best practices for data migration in legacy systems have proven particularly effective:

• Early involvement of all parties: By involving business units, IT leaders, and external experts from the outset, we gain decisive insights into historical data and possible stumbling blocks. Joint workshops or kick-off meetings create transparency and avoid misunderstandings.

• Use a multi-stage test environment: We use a tiered test architecture — from development through staging to production environments. We thus ensure that possible errors in scripts or configurations can be detected and corrected before they reach live operations.

• Closely managed risk management: Every project carries uncertainty. Systematic risk management uncovers technical and organizational hazards. We see data backups, compliance requirements, and automated monitoring of data integrity as critical points in particular.

• Focus on performance tests: Especially with larger data volumes, a migration can impair system performance. We recommend conducting performance benchmarks beforehand and checking whether the new hardware or cloud environment is sufficiently dimensioned. Beyond pure data movement, we also take into account changing user loads, for example when business processes run in parallel.

• Training and change management: Only when the workforce understands the new applications and processes can a smooth transition succeed. Sound change management is essential here. We frequently rely on training, webinars, and internal help documentation. The initial skepticism toward "the new" is thus reduced in a targeted way.

A textbook example of successful migrations are companies that gradually transfer old on-premises systems to modern cloud architectures. According to Ramotion (2025), many organizations succeed in achieving better scalability and security through newly set up cloud solutions [4]. They also frequently reduce technical debt — a topic we explored more deeply in our article on reducing technical debt.

Comparison of Common Migration Approaches

Below is a brief overview of the most common migration methods that we frequently evaluate in projects:

| Approach | Description | Advantages | Disadvantages | |---------------------------|----------------------------------------------------------------------|-------------------------------------------------------|-----------------------------------------------------------------------------------| | Big Bang | Complete replacement of the legacy system on a single cutover date | Rapid switchover, no parallel operation | Higher risk of errors and outages, extensive upfront planning needed | | Incremental | Step-by-step migration of individual modules or data areas | Lower risk, gradual testing and adjustment | Longer overall timeframe, high coordination effort | | Trickle (Parallel) | Old and new systems run alongside each other temporarily | Control of both systems in parallel, short downtime | Double maintenance effort, synchronizing data can be complex |

As we can see, there is no "one-size-fits-all" approach. Instead, the respective project environment determines whether a big-bang approach or incremental migration is more suitable. With highly complex structures, we occasionally turn to the strangler fig pattern migration to gradually replace certain functionalities in the background during ongoing operations.

Ensuring Data Protection and Compliance

A very important aspect when modernizing and migrating legacy systems is data protection. In many industries — from healthcare to the financial industry — strict legal requirements apply that should be in focus from the design of the target architecture. Thanks to newer technologies, encryption, role and permission systems, and audit trails can be implemented more easily than in outdated system environments.

We ensure compliance with the applicable General Data Protection Regulation (GDPR) and other industry-specific requirements at every step. Data is only transported encrypted, user permissions are carefully delimited, and sensitive data is not retained longer than necessary. With cloud environments, we additionally check whether the provider meets the required certifications and security standards.

A widespread problem is compatibility gaps between legacy data formats and modern platforms. This can lead not only to data loss but also to unwanted disclosures. Skillfield already pointed out the risks of poorly planned data integration in 2024 [3]. We therefore pay particular attention to conversion tables, standard formats (e.g. XML, JSON), and consistent validation steps in architecture planning. We thus ensure that sensitive information does not fall into the wrong hands or is accidentally manipulated.

The topic of compliance also concerns us in two respects: on the one hand, we want to comply with current standards and regulations. On the other hand, the modernized system opens up entirely new possibilities for automated monitoring, e.g. with regard to fraud detection metrics or data protection reporting. This automation creates transparency and makes the company overall more resilient to future changes in legislation.

Long-Term Optimization and Cultural Change

The migration itself is only the beginning. Once the new system is established, the goal is to secure the benefit in the long term and continue to expand it. It is particularly important that IT teams and users continue to develop in the new technological environment and can apply new methods. Only in this way do we achieve sustainable improvements in everyday business.

A key goal is often to gradually transfer monolithic legacy systems into microservice-based architectures. We have compiled more information on this in our article on monolith to microservices. This architecture allows us to deliver new functions faster and to scale individual modules independently of one another as needed.

Frequently underestimated is the cultural change such a modernization initiative brings with it. Employees must learn new technologies, leave behind ingrained processes, and embrace different ways of working. A leadership understanding that emphasizes transparency, communication, and willingness to learn helps us significantly here. An open dialogue with the involved business units and regular feedback are our cornerstones, so that everyone recognizes the meaning behind the migration and can actively participate.

In terms of cost reduction, it is also worth exploring the overall potential. Our experience shows that significant savings are often possible through process automation in production and administration areas as well as more efficient IT structures. Especially when old hardware is dismantled and decentralized server landscapes are consolidated, costs for maintenance and licenses can be greatly reduced after migration — more on this in our article on reducing maintenance costs of legacy systems.

Last but not least, modernization and the replacement of old technologies contribute to reducing security risks. Outdated operating systems or databases often can no longer be adequately patched, which opens entry points for attackers. By modernizing existing legacy systems, we close critical security gaps before greater damage can occur.

Conclusion and Outlook

Data migration without data loss is more than just a concrete IT project for the German mid-market. It forms the cornerstone for overcoming outdated structures, reducing growing technical debt, and sustainably setting the course for future-proof digitalization. Anyone who carefully plans and implements their data migration in legacy systems benefits from powerful platforms, improved IT security, and an overall more efficient handling of business.

A switchover always involves certain risks — on the one hand with regard to data loss, on the other hand with regard to system availability. But our experience shows that with structured planning, early involvement of all stakeholders, and professional test and risk management, these risks can be reduced to such an extent that the advantages far outweigh them. Complete documentation, parallel pilot projects, and close monitoring ensure that no data is lost and that ongoing operations remain largely undisturbed.

We view data migration and system change as a dynamic process that can sometimes extend over months or years — depending on corporate goals, the degree of modernization, and individual risk appetite. Especially with regard to upcoming technologies such as artificial intelligence and automated decisions, new opportunities arise when the data foundation is designed for it. Companies that already replace their legacy systems today and use modern infrastructures are significantly better prepared to deploy these technologies in a targeted way.

Finally, it should be mentioned that the cultural level also strongly determines the success of a migration. A team that supports the step into the new transforms potential resistance into a will to shape. Training, intensive knowledge transfer, and close exchange between IT and business units create a climate that fosters further development. Acceptance grows when we put not only the risks but above all the opportunities in the foreground.

One thing is therefore clear: a solid data migration not only protects against data loss but also lays the foundation for all future digitalization initiatives. Anyone who is ready to consistently take this path gains flexibility, economic efficiency, and competitiveness — today and in the coming years. By proceeding deliberately and placing equal value on technology, processes, and people, we successfully master the system change and thus sustainably secure the future of our company.