Data migration is a critical but often underestimated aspect of business transformation, particularly when migrating to complex systems like SAP. Organisations must manage vast amounts of data, transforming it into new formats and structures. The process can overwhelm teams without careful planning, leading to delays, errors, and cost overruns. This blog explores how tools like Pulse can help streamline the migration process, reduce risks, and ensure a smooth transition to your new systems. 

Many organisations approach data migration with the misconception that it’s simply a task of moving data from one system to another. A common assumption is that if the data works in the old system, it should work in the new one as well. However, this fails to recognise the fundamental differences between legacy systems and advanced platforms like SAP. 

Legacy systems often rely on manual intervention to function despite incomplete or poor-quality data, masking underlying issues. However, SAP and similar systems demand high-quality, structured data to automate processes efficiently, creating a significant gap in readiness. For example, Pulse has uncovered extensive hidden data quality issues during the migration process that manual fixes in legacy systems had concealed. The scale of missing and incorrect data required significant enrichment to meet SAP’s stringent automation requirements. 

Data migration is rarely a one-off activity. The data that exists at the start of a project is not the same data that will be in place at go-live. Businesses are continually creating new data—new customers, products, transactions, etc. This constantly evolving data landscape means that migration teams must extract, validate, and transform data and repeatedly cycle through these processes. This generates multiple versions of data, creating a huge management burden. 

Without proper controls, this can spiral into chaos, where teams are overwhelmed by the sheer volume of data fixes and updates. Data requirements frequently evolve in projects spanning 6–18 months as businesses adapt to new processes or regulations. A robust tool like Pulse allows for configuration changes to be implemented centrally, ensuring updates are seamlessly reflected across all data objects without introducing inconsistencies. 

Attempting to manage data migration manually, particularly for complex transformations like SAP, are less efficient and riskier. Teams can end up managing hundreds of spreadsheets, each containing different sets of data errors, enrichment requirements, or transformation issues. Every week, new versions of these data sets need to be validated and enriched, which can only become manageable with the right tools and established automated processes. 

Programmes that rely solely on manual processes frequently lose control over data quality. Often this leads to delays in test cycles as teams struggle to fix data in time. The result is wasted time and resources, pushing back critical phases like System Integration Testing (SIT) or User Acceptance Testing (UAT). 

Moreover, it is likely that to save these time critical activities a solution of “manufacturing dummy data” is suggested. This is a form of “kicking the can down the road” and the fundamental issues behind data readiness are not understood or resolved. In our view this invalidates the solution testing being completed, as it is missing the key component of genuine business data. 

Organisations that replaced manual processes with automated tooling reported up to a 30% reduction in project timelines, as tools eliminated bottlenecks and streamlined error tracking. 

The complexity of data migration requires a combination of people, processes, and tools. While tooling is not a silver bullet, it significantly reduces manual effort and accelerates the migration process by automating repetitive tasks. Tools like Pulse, for example, can remove the reliance on spreadsheets and email chains by providing a centralised platform for managing data. 

Tooling can automate data extraction, transformation, and validation processes. It can also provide real-time reporting and audit trails, ensuring that teams have visibility into data quality, progress, and errors. This level of automation allows organisations to make data-driven decisions faster, reducing the risk of delays and errors. 

One of the most significant advantages of tooling is prioritising tasks based on business needs. In a manual process, teams are often inundated with thousands of data errors but have no way to prioritise which issues need fixing first. Tools can help teams triage data issues, ensuring that the most critical problems are addressed first. For example, an organisation might prioritise fixing data for direct vendors over aftermarket vendors. A good tool allows for this kind of configuration, helping businesses focus on high-impact data objects, thereby preventing bottlenecks during migration. 

Data quality issues such as duplicates and inconsistencies can severely impact operational efficiency in the new system. Legacy systems often contain multiple records for the same customer or vendor, which can lead to errors in reporting and process inefficiencies post-migration. 

A robust data migration tool can identify and manage duplicates through harmonisation and deduplication. Advanced features, such as fuzzy matching, help detect potential duplicates even when data is not identical, enabling teams to merge records accurately. For example, harmonisation through Pulse  reduced duplicate records by 40% for a manufacturing client, significantly improving reporting accuracy and minimising operational errors. 

Once the data has been loaded into the new system, it’s crucial to ensure that it was migrated correctly. Reconciliation processes compare data between the source and target systems, ensuring no critical information was lost or misrepresented during migration. This is particularly important for financial or manufacturing data, where even minor discrepancies can have a significant operational impact. 

A tool like Pulse  can automate reconciliation processes, providing businesses with confidence that their data is accurate and ready for use. With this assurance, businesses can avoid operational failures post-go-live, from halted production lines to incorrect financial reporting. We have seen examples where in manufacturing where data reconciliation issues post migration temporarily halted production, costing the company an estimated £100,000 per hour. Using field-by-field reconciliation during migration can prevent such costly disruptions. 

The timing of data migration is critical to the overall programme timeline. Testing phases and go-live windows are tightly scheduled, and any delays in data readiness can derail the entire project. Tools can provide the predictability needed to avoid such disruptions by simulating data loads and identifying issues before they become critical. 

Tools like Pulse  help ensure teams are fully prepared for production loads by consistently validating data and simulating migration scenarios, avoiding last-minute surprises. Simulations conducted via Pulse  reduced production disruptions by 25%, providing organisations with accurate load times and effective cutover planning. 

In addition to managing the technical aspects of migration, businesses must also ensure that sensitive data is protected. Data migration tools should offer security features such as data masking to ensure that only authorised personnel can access sensitive information. This is particularly important for organisations handling financial, HR, or personally identifiable information (PII). 

The key to de-risking your data migration process lies in automation, robust processes, and skilled people. Tools like Pulse  provide the backbone of an effective data migration strategy, offering automation, visibility, and predictability. However, even the best tools cannot succeed without the right processes and a team that understands the complexities of data migration. 

By investing in the right tooling and building a robust data migration framework, organisations can avoid the common pitfalls of migration projects, ensuring a smooth transition to their new systems and realising the full potential of their transformation programmes.