A successful ERP project starts with data.
Manage your business-critical data migration in a controlled manner. Avoid project delays, implementation issues, and data errors before go-live.
ERP implementation problems are often caused by data rather than technology. We help ensure that the migration is completed on time and that the data in the new system works from day one.
Assess your migration readiness. Ensure that the new system is built on reliable data, clear ownership, and effective processes. Talk to an expert.
The ERP project is delayed. The budget is exceeded. Trust suffers.
Many problems in ERP projects and system renewals are not caused by technology. They are caused by the data on which the new system is built.
When data ownership is unclear, data quality is insufficient, or migration preparation begins too late, the problems become visible during testing or, at worst, at go-live.
The new system inherits existing data problems
The new system’s data requirements
Inconsistent master data
Problems are identified and corrected too late, close to go-live
Manual data cleansing
Lack of a governance model in the migration project
The number of Excel files
Unclear ownership
Why do migration projects fail?
Most ERP and system renewal projects start with technology. However, the real risks are often found in the data.
Data, processes, and ownership.
Poor data does not disappear when the system is changed. It simply moves with the migration and creates new problems if the requirements, data model, and processes of the new ERP system do not align with the data in the legacy system.
The greatest risks are not related to technology, but to data quality, ownership, decision-making, and process management. When these are addressed and managed correctly during the migration, implementation is faster, risks are reduced, the data is ready well in advance, and the new system delivers value from day one.
Technology moves data in a controlled and transparent manner. The ERP project determines which data should be migrated, at what level of quality, and according to what schedule.
A successful migration project
A successful migration is not a separate technical phase of an ERP project. It is built step by step on data quality, business decisions, and controlled implementation.
Understand the current state
Before the first migration, we assess data quality, scope, ownership, and the greatest risks.
- Migration scope
- Data quality
- Risk identification
- Business ownership
Prepare the data
Not all data should be migrated. We ensure that the data to be migrated is high-quality, consistent, and aligned with the requirements of the new system.
- Improving master data quality
- Removing duplicate data
- Standardizing data
- Defining business rules
Execute the migration in a controlled manner
Data is transformed, validated, and tested before implementation.
- Mapping and data model alignment
- Data transformations
- Test migrations
- Validation and approval
Ensure continuous governance
Migration is a project. Data governance is a continuous capability.
- Data ownership
- Governance models
- Quality monitoring
- Continuous improvement
A successful migration does not begin with transferring data. It begins with deciding which data should be migrated, at what level of quality, and with which business objectives.
The business benefits of a successful migration
A successful migration is not the end point of a project. It is the foundation for a new ERP system, more efficient processes, better decision-making, and future AI solutions.
Faster ERP implementation
Avoid project delays, additional correction rounds, and last-minute surprises. High-quality data accelerates implementation and reduces risks throughout the project.
Better user experience
Users trust the new system when customer, product, and supplier data is accurate from day one.
More reliable planning and decision-making
High-quality data improves forecasting, planning, and operational decision-making throughout the organization.
Lower project costs
Less manual correction work, fewer errors, and lower costs for resolving problems after implementation.
A ready foundation for automation and AI
AI is only as reliable as the data it uses. A well-executed migration creates the foundation for future automation, analytics, and AI solutions.
A poorly managed data migration can lead to significant problems during system implementation. At worst, it can put business-critical processes, such as invoicing, at risk. A well-managed data migration enables business benefits from day one.
How ready are you for the migration?
Many ERP and system projects begin before the starting point of the data is fully understood. The more items you can answer yes to, the lower the migration risks will be.
- Data owners and responsibilities have been defined
- Source systems and data sources have been mapped
- Critical master data has been identified
- Data quality has been assessed
- Business rules have been documented
- The scope of the migration has been agreed
- The testing and acceptance plan is ready
- The data governance model has been defined
If even one of these items is unclear, the risk can easily shift to the later stages of the migration, when resolving it is slower and more expensive.
Are you ready for the migration?
Identify the most important risks before implementation and ensure that the project progresses according to a realistic schedule.
When is data migration needed?
Data migration is often one of the most critical phases in business transformation. A successful migration ensures that business-critical data is transferred to the new environment securely, in a controlled manner, and ready for use.
ERP renewal
When moving to a new ERP system, existing data must be aligned with the data models, processes, and business requirements of the new system.
SAP, Microsoft Dynamics, Business Central, IFS, Odoo, Oracle, and other ERP solutions.
Master data management
Consistent master data requires data to be harmonized, cleansed, and consolidated from multiple source systems.
Product information management (PIM)
Migrating and harmonizing product data enables more efficient product information management and a better customer and e-commerce experience.
Mergers and acquisitions
During mergers and acquisitions, customer, supplier, product, and financial data must be consolidated in a controlled manner without disrupting business operations.
System consolidation
Consolidating data from multiple systems, databases, or business units into one operating model.
Data migration is not only a phase of an ERP project. It is a critical part of almost every major business transformation initiative.
Migration is a project. Data Control is a capability.
Many organizations complete the migration successfully but soon encounter the same data problems again.
Incorrect data, unclear ownership, and inadequate operating models gradually become part of everyday work with the new system. As a result, data quality deteriorates, user trust declines, and the business benefits gained from the system remain lower than expected.
Data Control ensures that data remains high-quality, managed, and aligned with business needs even after the migration project.
Data Migration
- Transfers data to the new environment
- Corrects identified data issues
- Supports a successful implementation
- A project-based initiative
- Transforms and harmonizes data for the target system
Data Control
- Maintains data quality
- Defines ownership and responsibilities
- Continuously monitors data quality
- Prevents problems from recurring
- A continuous operating model
Migration moves data to the new system. Data Control ensures that the data remains high-quality afterwards and creates a reliable foundation for automation, analytics, and the use of AI.
Migration is the beginning. Data Control ensures continuous quality.
Build a foundation for continuous data management, reliable analytics, and the use of automation and AI.
Explore the Data Control solution and build a permanent capability for ensuring data quality, ownership, and governance. High-quality data enables reliable analytics, effective automation, and the use of AI in business.
FAQ
How long does a data migration project take?
The duration of a data migration depends on the volume and quality of the data, the number of systems, and the scope of the project. The greatest delays are usually not caused by technology, but by correcting data quality issues, making business decisions, and inadequate preparation.
When should data cleansing begin?
As early as possible. The later data quality issues are identified, the more additional work, risks, and project delays they cause. Data assessment should begin during the planning phase of the ERP or system project.
Who should own migration decisions?
Technology enables data transfer, but the business decides which data is migrated, at what level of quality, and according to which business rules. Data ownership should therefore remain with the business, not solely with the IT organization.
How much historical data should be migrated?
It is generally not advisable to migrate all data. The amount of historical data depends on business needs, reporting requirements, regulation, and the intended use of the new system. The most important task is to identify which data will continue to create business value in the new environment.
Can AI help with data migration?
Yes. AI can support tasks such as data classification, comparison, quality analysis, and anomaly detection. However, AI does not replace business decisions about which data should be migrated and at what level of quality.
What is the greatest risk in data migration?
The greatest risks are usually related to data quality, unclear ownership, inadequate business rules, and problems being identified only during the final stages of the project.
Why do ERP migrations fail?
ERP migrations rarely fail because of technology. The most common causes are poor data quality, unclear responsibilities, inadequate preparation, and incompatibility between existing data and the requirements of the new system.
Is Data Control needed after the migration project?
Yes. Migration transfers data to the new system, but it does not prevent new data issues from arising. Data Control helps maintain data quality, ownership, and governance after the project and creates a foundation for analytics, automation, and the use of AI.
Before migrating your data, make sure it is worth migrating.
High-quality data, clear responsibilities, and controlled decisions are the foundation of a successful migration. When the data is in order, implementation is faster, risks are reduced, and the new system begins delivering value from day one.