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Discovery & audit
We identify source systems, data owners, integrations, volumes, constraints and business requirements.
We design and execute data migrations across systems, databases and environments. Our approach combines business discovery, source-to-target mapping, data cleansing, automated transformations, trial migrations, validation and controlled production cutover.
Data migration is both a business and a technical project. Success depends on source-data quality, relationship integrity, transformation rules, acceptance criteria and a controlled cutover plan.
We identify source systems, data owners, integrations, volumes, constraints and business requirements.
We define source-to-target mapping, conversion rules, dictionaries, identifiers and exception handling.
We identify missing values, duplicates, inconsistencies and invalid formats and agree cleansing rules.
We test migration mechanisms before production, measure outcomes and eliminate process defects.
We compare records, relationships, documents and critical business values against agreed criteria.
We plan production switch-over, backup, rollback, monitoring and the initial stabilization period.
Each stage has a defined objective, output and decision gate. The approach scales from a single database to complex multi-system environments.
Scope, objectives, systems and risks.
Data profiling and quality assessment.
Source-to-target and transformation rules.
Data preparation and correction.
Dry run and defect measurement.
Technical and business validation.
Controlled production switch-over.
Monitoring, fixes and project closure.
There is no single model that fits every organization. The right approach depends on acceptable downtime, system landscape, data-change velocity and the ability to run environments in parallel.
A one-time migration and switch-over. Operationally simple, but it requires a well-controlled maintenance window.
Data is moved by module, business unit or cohort. Risk can be reduced and lessons applied to subsequent waves.
Legacy and target systems operate side by side for a defined period, enabling result comparison and high continuity.
Initial bulk migration followed by change synchronization until final cutover, minimizing production downtime.
We provide reusable EINTELLIX templates to structure pre-migration analysis and the first technical workshops. They are generic and contain no client-specific information.
A structured questionnaire covering source systems, data quality and volume, integrations, security, migration window and acceptance criteria.
A one-page project card organizing objectives, scope, priorities, responsibilities and the next steps of a migration project.
A comparison of Big Bang, phased migration, Parallel Run and incremental synchronization models with selection criteria.
An end-to-end process covering Discovery, Audit, Mapping, Cleansing, Trial Migration, Validation, Cutover and Stabilization.
A workshop agenda with decision log, action owners and a structured list of open topics.
For critical systems, a migration plan must cover more than data transfer. It must address continuity, recoverability, access control and a complete validation trail.
We support projects involving business-system migrations, databases, on-premise and cloud environments, document repositories and API-connected platforms.
EINTELLIX also delivers database design, system integrations, backend development, process automation and post-migration software development.
Start with a focused discovery and source-system assessment. We can then define the right migration model, test scope and commercial approach.