Synchronise data
Two systems.
One consistent data state.
Customer, product or stock data needs to match across multiple applications. We define which system is authoritative, when data is transferred and how conflicts are handled.
Discuss your projectThe starting point
The data exists.
So does the workaround.
A customer is updated in the CRM while the ERP still holds the old address. Two data states cause inquiries, incorrect documents or duplicate maintenance. Synchronization therefore needs more than a regular copy: it needs clear rules.
What we clarify together
- Define the authoritative system for each data field
- Map records unambiguously using stable keys
- Distinguish new, changed and deleted entries according to business rules
- Define transfer intervals and conflict rules
- Make discrepancies and failed transfers visible
One possible scenario
Maintain the customer address just once.
The CRM holds contact details, while the ERP holds billing information. Agreed field rules transfer changes in the correct direction and flag conflicting values for clarification.
Illustrative example; no claim of an already delivered customer project.
For technical assessment
The technology follows
the task.
We evaluate these approaches according to your data and your systems' capabilities.
Unambiguous mapping
Customer numbers, product numbers or agreed IDs link records. Without stable keys, mapping must first be clarified.
Schedule or event
Periodic transfer or a webhook after a change: requirements and interfaces determine the approach. Real-time is not an end in itself.
Conflicts and retries
An interrupted connection must not cause uncontrolled duplicate entries. Repeatability, logs and conflict decisions are planned from the start.
Before we start
Good to know.
Your specific starting point determines the right solution path.
Is synchronization in both directions possible?
Yes, if the applications and business rules support it. The deciding system for simultaneous changes is especially important. A clearly defined direction for each data field is often sufficient.
How current can the data be?
This depends on interfaces, data volumes and operational requirements. Fixed intervals or event-driven transfers may be suitable. We define the required freshness before implementation.
How do you prevent duplicate records?
Through unique keys, checks before creation and repeatable transfer steps. Existing duplicates must be cleaned up before or during implementation using clear rules.
What happens when a system fails?
We plan retries, logs and, where appropriate, a queue. After recovery, pending transfers can be completed in a controlled way. The exact behavior is defined for each project.
The next consideration
Perhaps there's
more to connect.
A few brief details are enough
Where is the latest information missing?
Describe which details do not match between your applications.
Your enquiry to Datenquelle
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