This article takes a practical look at which types of data are relevant in a company, how data migration and data management work – and why the highest data quality is not a luxury, but a necessity.
Types of data and their significance
In ERP systems (and beyond), companies encounter various types of data that differ in terms of their use and maintenance:
Master data
- Examples: Customers, suppliers, articles, materials, price lists, parts lists
- Significance: Master data is the basis of all processes. Incorrect master data has a direct impact on orders, invoices, production or reporting.
- Challenge: High change frequency, e.g. for customer addresses or article information.
Transaction data
- Examples: Orders, postings, goods receipts, production orders, payments
- Significance: This data is generated by business processes. They document what happened when and where.
- Challenge: Volume grows rapidly, high demands on performance and archiving.
System and control data
- Examples: User rights, workflows, parameterizations, customizing settings
- Significance: This data controls the behavior of the ERP system itself. Incorrect settings can block processes.
- Challenge: Strong dependencies that require in-depth know-how.
Analysis data
- Examples: Reports, KPIs, dashboards, BI evaluations
- Significance: Analyses help to make strategic decisions. Their quality depends directly on the accuracy of the underlying data.
- Challenge: Consolidation of different data sources, data harmonization.
Data migration: a clean start to the new system
Data migration is a critical success factor, especially during an ERP implementation or a release change. This involves transferring data from legacy systems to the new ERP system. Typical steps:
- Analysis of the source data: What data needs to be transferred? What is the quality of the data?
- Mapping & transformation How do old data structures fit into the new system? Do fields need to be renamed, values converted or merged?
- Cleansing & duplicate check Removal of incorrect or superfluous data records.
- Test migrations: Multiple test runs to check data completeness and quality.
- Go-live migration: Transfer of data to the production system, often over a defined time window.
Practical tip: The importance of data migration is often underestimated. However, it often determines how quickly and stably a new ERP system goes live.
Data management: Processes for sustainable data quality
Once data has been migrated cleanly, the work is far from done. Data maintenance is a continuous process. Important elements of successful data management:
Governance & responsibilities
- Who is allowed to create or change data?
- Who checks the data quality?
- Are there approval processes?
A lack of responsibilities often leads to “data silos” and inconsistent information.
Standardization
- Clear specifications for data formats (e.g. spelling of addresses, article numbers)
- Mandatory fields to secure important information
Example: Without standardized spelling for country names (“Germany”, “Deutschland”), systems cannot consolidate data cleanly.
Tools & automation
- Duplicate check
- Plausibility checks during data entry
- Workflows for data release
- Master data portals for decentralized maintenance with central quality assurance
Employee training
Data quality is not a purely technical task. Users also need to know
- Which fields are mandatory
- The consequences of incorrect entries
- Which standards apply
Why the highest data quality is essential
Poor data quality often has more far-reaching consequences than are apparent at first glance:
- Errors in production: incorrect parts lists lead to incorrect production orders.
- Financial losses: Incorrect invoice addresses delay payments.
- Poor decisions: Reports and KPIs are based on incorrect figures.
- Unnecessary costs: Time for manual corrections, clarifications or returns.
Well thought-out data management significantly reduces these risks. Companies that take their data quality seriously benefit from
- More efficient processes
- Faster decisions
- Higher customer satisfaction
- Better compliance
Especially in times of AI and automation, modern technologies can only reach their full potential on the basis of clean data.
Summary:
Whether ERP implementation or ongoing operation: data management is a key success factor for every company. It ensures that processes run efficiently, analyses deliver reliable results and the company can react flexibly to new challenges. The decisive factor here is not only clean data migration, but also the continuous maintenance and monitoring of data quality.
Those who take care of clearly defined processes, responsibilities and suitable tools at an early stage lay the foundation for a high-performance ERP system – and for long-term business success.
