Data Migration

Data loss-free migrating

When switching systems, ERP or the cloud, we bring your data safely to its destination – including cleaning, mapping and quality assurance.

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Clean & Complete1:1 validatedAudit-proof & GDPR
Data quality100% migrated
Müller GmbH · Müller GmbhDuplicatesremoved
contact@companyinvaliditycorrected
PLZ —missingcomplementary
1:1 validated-90% duplicatesProtocol

When data becomes a stumbling block

System changes rarely fail due to technology – usually due to data: duplicates, inconsistencies and missing mappings jeopardize project and operation.

  • Duplicates and inconsistent data sets falsify evaluations.
  • Lack of mapping leads to data loss during system change.
  • Growing old data is incomplete and poorly documented.
  • Migration errors are only noticeable in productive operation.

When is a data migration

The trigger is different, the actual work is the same every time – and it lies in the data, not in the new system:

  • Replacement of an ERP or CRM systemFor example, because the manufacturer stops maintenance or switches to a pure cloud model.
  • Shutdown of own developmentwhich no one can wait anymore, because the developers from back then long ago work elsewhere.
  • Moving to the Cloud, in which stock data must migrate and the order of adjustment and migration must be determined consciously.
  • Consolidation of multiple systems after purchase or restructuring – the most complex case, because the same objects are coded differently in each source.
  • Archiving of history, which in the production system only causes costs and duration.

In all five cases, the same order applies: first know what is in the data, then decide what comes along. Whoever turns it around, migrates the problems. This is more detailed in the contribution Lossless data migration during system change.

What “loss-free” means concretely

“Lossless” sounds self-evident and is still worthless as a requirement as long as no one says what you measure it by. We attach the term to three proofs provided before the go-live – each of them verifiable, none of them a matter of trust.

Completeness

Every technically relevant data set from the old system exists in the target system. Quantity scaffolds and sum comparisons per object are checked: If there are 84,312 documents in the old system and 84,107 in the target system, the difference must be explainable sentence by sentence – as deliberately excluded test data, as removed duplicates or as an error that we fix.

Reference and semantic integrity

An invoice without associated customers is formally migrated and technically unusable. We therefore not only check data sets, but their relationships to each other. Heicers are silent shifts in meaning: a status field with five forms meets a target system that only knows three. Technically, this can be mapped – whether the evaluation says the same afterwards, decides your specialist department and not the migration tool. We disclose such cases instead of burying them in mapping.

Traceability

For each data set in the target system, it is documented from which source it comes and which transformation it has undergone. This is not a diligent task for the project files, but the prerequisite for being able to react to errors in a targeted manner at all – and in regulated environments anyway a duty.

Our services around data migration

From analysis to cleanup to validated transfer – reproducible and traceable.

Analysis and mapping

We analyze source and target structures and define a clean field mapping.

Cleaning & Quality

Remove duplicates, normalize values and measurably ensure data quality.

Safe transfer

Migration with test runs, validation and audit-proof matching.

With ALGEBRA instead of migration at your own risk

Migration without a concept Data Migration with ALGEBRA
Data quality Duplicates & gaps travel with Adjusted, normalised, enriched
Procedure Unique, manual Reproducible ETL lines
Security Errors only visible in operation Test runs & 1:1 comparison before Go-Live
Evidence No documentation Audit-proof protocol

How we migrate your data

Reproducible from the source to the controlled go-live – each step is validated and documented in an audit-proof manner, during operation.

SourceAlt-System & Files
SAPDynamicsOracleMS SQL
Analysis and mappingAssign fields
CleaningDuplicates · Normalization
Test runValidation & 1:1 reconciliation
Go-livecontrolled and documented

Measurable benefits

100%
validated migration with protocol
-90%
fewer duplicates after cleaning
1:1
Comparison Source ◯ Objective

Key figures from real ALGEBRA projects and studies. Concrete results depend on the use case.

What distinguishes our data migration

  • Analysis of source and target systems incl. Field mapping
  • Connection of ERP/CRM (SAP, Dynamics, etc.), databases and files
  • Data cleansing: duplicates, normalization, enrichment
  • Automated ETL lines for repeatable migrations
  • Validation, test runs and audit-proof matching
  • GDPR-compliant processing, on-premise possible

Related benefits

Cloud Migration

Securely modernize legacy systems into the cloud.
Find out more

Business Intelligence & ETL

From data reliable key figures and dashboards.
Find out more

AI Consulting

RAG systems & AI agents from your knowledge.
Find out more

Frequent questions about data migration

ERP and CRM systems such as SAP or Microsoft Dynamics, relational databases, file and export formats and individual legacy systems. What matters is less the product than the access: An open database behaves completely differently than an application from which data can only be extracted via report exports. Both are feasible, the effort differs considerably.

Through fully documented mapping, at least one trial migration on the real stock and a 1:1 comparison against the old system before the go-live. Each field without a corresponding in the target system is either a conscious, recorded decision or a later data loss – there is no third.

Seriously, this can only be said after the analysis, because two variables are decisive: the number of different data objects and the accessibility of the old system. Reliable, on the other hand, is the distribution of the effort – analysis, mapping and cleaning regularly take up a multiple of the actual transfer. The script that writes the data is built in days; Clarifying what “active customer” means in both systems takes weeks and needs your specialist department. Projects are almost never delayed because the technology was too slow, but because technical decisions were made too late.

We advise against flat rates – they assume that someone already knows the state of the data, and this is never the case before the analysis. The costs drive four factors: the number of different data objects (not the amount of data – ten million documents of a type are simpler than fifty object types with a thousand sets), the status of the old data, the extent of the history to be taken and the number of connected surrounding systems. We therefore start with an analysis that turns estimates into reliable figures.

No – and this question is the most effective cost lever in the whole project. “Do we really need twelve years of movement data in the new system?” is uncomfortable because nobody wants to answer it alone. History that moves into an archive instead of into the productive system costs a fraction and still remains accessible. We clarify this together with the specialist department and, where necessary, with regard to retention periods.

We clean and normalize as part of migration – duplicates, gaps and inconsistencies are measurably reduced, not claimed. The time is important: The cleanup can start before the target system is even established. Duplicates can be eliminated in the old system, and this work is not in vain in every scenario.

Yes. The transfer runs on automated, versioned routes – test runs and final migration are reproducible and deliver the same result with the same initial stock. This is also the basis for having a defined way back on the go-live weekend instead of having to improvise.

Is there a system change?

Let us analyze your data and set up a secure, traceable migration plan.

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