ERP-to-ERP data migration, ETL field mapping, multi-cycle validation, cutover planning, and rollback procedures - delivered by a team that's done this in production, not just on paper.
ERP-to-ERP migrations - from legacy systems to Dynamics 365 Finance & Operations, SAP, or Oracle - are among the most complex data engineering challenges in enterprise IT. The data models are different, the business rules are implicit, the source data is messy, and the cutover window is small.
At DynamicUnit, we treat data migration as an engineering programme, not a data dump exercise. We profile the source data, build field-level ETL mappings, run multiple mock migration cycles with reconciliation reports, and prepare detailed cutover runbooks with tested rollback procedures. By the time we execute the production migration, it's a rehearsed performance - not an improvisation.
Source data is rarely clean enough to migrate as-is. Our data cleansing team works alongside the migration engineers to profile, deduplicate, and standardise records before they enter the pipeline. For migrations that feed a new analytics platform, we also design the data warehouse layer so historical data lands in the right structure from day one.
We've migrated data into Business Central, Hexagon EAM, and cloud databases — always with structured validation so opening balances are right on day one.
BOM, inventory, vendor masters, and open PO migration into Dynamics 365 F&O or Business Central - with opening balance validation.
Asset registers, work order history, and maintenance schedules migrated into Hexagon EAM from Maximo, Infor, and custom CMMS platforms.
Chart of accounts, GL balances, customer and vendor masters, and transactional history migrated with full reconciliation and audit-ready documentation.
Product catalogues, pricing, customer data, and order history migrated across platforms - including e-commerce re-platforming projects.
From data profiling and ETL mapping to production cutover and post-migration support - here's how we run a migration.
Analyse source data for completeness, consistency, duplicates, and format issues before migration - identifying problems early rather than discovering them on cutover night.
Document every source-to-target field mapping, transformation rule, lookup, and default value - producing a mapping specification agreed with both business and technical teams.
Resolve duplicates, standardise formats, fill required fields, and apply business rules to the source data before it enters the migration pipeline.
Run multiple full mock migrations into a target environment, with full reconciliation reports comparing source record counts and key values against migrated output.
Automated row count checks, financial value balancing, and business-rule validation scripts that confirm migrated data is accurate before cutover sign-off.
Detailed cutover runbooks with timed tasks, ownership, go/no-go decision points, and communication plans - so every team member knows exactly what to do and when.
Tested rollback runbooks that can restore the source environment to operational state if the production migration encounters a critical issue during the cutover window.
Named engineers on standby for the hypercare period - resolving data issues, running additional reconciliation, and addressing user-identified discrepancies after go-live.
Most data migration failures are not technical failures - they're planning failures. Insufficient validation cycles, undocumented rollback procedures, and assumptions about data quality are the common culprits. Here's how we avoid them.
We profile the source data first - finding nulls, duplicates, invalid formats, and broken references before the ETL mapping is written, not after it's built.
We run at least three mock migration cycles - each with full reconciliation - so by cutover night the script is a rehearsed procedure, not a first attempt.
Mapping documents and reconciliation reports are reviewed and signed off by business stakeholders - so problems are caught before production, not during it.
Rollback procedures are tested in the same environment as mock migrations - so if we need to use them, they work. Not theoretical. Actually tested.
Data extracted from production systems is encrypted in transit and at rest, access-controlled during transformation, and scrubbed from migration environments post-go-live.
We run the programme across technical teams, business owners, and project managers - ensuring nobody finds out about a data issue for the first time on cutover day.
We profile every source table - completeness, duplicates, format issues, and referential integrity. You get a data quality assessment and a realistic migration scope document.
We build field-level mappings, transformation rules, and cleansing logic. Every mapping is reviewed and signed off by business stakeholders before development.
We run at least three full mock cycles with reconciliation reports - record counts, financial totals, and business-rule checks - until the output is verified and the timing is proven.
We execute the rehearsed cutover, run final reconciliation, and provide 2-4 weeks of hypercare with engineers on standby. Tested rollback procedures are ready throughout.
Tell us your source and target systems, your timeline, and your biggest concerns - we'll walk you through a realistic migration plan and what it takes to do this safely.
