ERP Data Migration Effort Calculator: Hours, Timeline, and Cost
This calculator estimates the effort, elapsed time, and cost of migrating data from a legacy ERP into a new platform, built for manufacturers moving off systems like Baan IV, SyteLine 8, or homegrown databases. Data migration is the most consistently underestimated workstream in ERP projects: teams budget for the extract scripts and forget the mapping debates, cleanup rework, and reconciliation cycles that consume most of the real hours. Enter your entity count, honest data-quality rating, history depth, and planned test cycles to get a defensible hours, weeks, and cost estimate.
Your numbers
Distinct master and transactional objects: items, BOMs, routings, customers, suppliers, open orders, inventory balances, etc.
Honest self-assessment. Most systems 10+ years old with no data governance belong in moderate or poor.
Transactional history depth. Converting more than 5-7 years is rarely worth it; consider an archive instead.
Full end-to-end test conversions before cutover. Fewer than 2 is a major go-live risk.
Blended internal plus consultant rate for the migration team.
Your results
Estimates only, based on typical mid-market manufacturing conversions. Actual effort varies with tooling, source system accessibility, and how many surprises your data is hiding. Use for planning, not fixed-bid pricing.
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The methodology behind the numbers
The model starts from a benchmark of roughly 24 hours per data entity, which covers field mapping workshops, extract development, transformation rules, load scripting, and validation queries. That figure comes from repeated mid-market manufacturing conversions and assumes reasonably accessible source data. Two multipliers then adjust it: data quality, because poor data doubles effort through cleanup and rework loops, and history depth, because every additional year of transactions adds reconciliation surface area. Finally, mock conversion cycles are costed separately at about 4 hours per entity per cycle, covering execution, reconciliation reporting, and defect triage. This structure mirrors how experienced migration teams actually scope the work, which is why the output tends to land close to real project actuals.
Benchmarks worth knowing
Before you trust any migration estimate, yours or a vendor's, compare it against what typically happens on real manufacturing conversions. Estimates fail in a characteristic direction: they price the scripting work, which is visible and technical, and skim past the cleanup, reconciliation, and decision-making work, which consumes the majority of real hours but shows up in no statement of work. A vendor estimate that covers 40 entities in 500 hours is not more efficient than this calculator; it is scoped for the happy path and priced to win. These reference points come from delivery data across Infor SyteLine, LN, and Baan projects and will quickly expose that pattern in any proposal you are holding.
- Data migration typically consumes 15-25 percent of total ERP implementation effort
- Teams that run fewer than two full mock conversions discover most defects at cutover
- Converting more than 5-7 years of transaction history rarely survives a cost-benefit test
- Cleanup of item, BOM, and customer masters routinely takes longer than all technical scripting combined
How to interpret and refine your estimate
With the default inputs of 40 entities, moderate quality, and three mock cycles, the tool lands around 2,000 hours and roughly 18 weeks for a three-person team, which matches a typical single-site manufacturing conversion. If your result looks surprisingly low, your entity count is probably missing transactional objects like open orders, WIP, lot and serial records, and quality history. If it looks alarmingly high, the levers that actually move the number are reducing history depth via archiving, cleaning the worst master data domains before conversion starts, and descoping entities that can be rebuilt rather than converted. Never respond to a high estimate by cutting mock cycles; that trades visible planning cost for invisible go-live risk.
How Netray accelerates data migration
Netray has migrated data out of Baan, SyteLine, and assorted legacy systems for aerospace, defense, and electronics manufacturers, and we have turned the repetitive parts into accelerators. Our AI-assisted profiling tools inventory and score your legacy data quality in days instead of weeks, our mapping libraries for Infor targets eliminate rediscovering standard transformations, and our reconciliation frameworks make every mock cycle faster than the last. That typically compresses the timeline this calculator predicts by 20-30 percent. If your estimate is bigger than your budget, talk to us before descoping test cycles; a specialist will show you which levers are safe to pull and which are not.
Frequently Asked Questions
What counts as a data entity for this calculator?
Any distinct object you must map and convert: item master, BOMs, routings, work centers, customers, suppliers, price lists, open sales and purchase orders, inventory balances, lot and serial records, open AR and AP, GL balances, and quality records each count as one. Most single-site discrete manufacturers land between 30 and 60 entities once transactional objects are honestly counted. Undercounting entities is the single most common reason real projects exceed early estimates.
How much history should we actually convert?
Less than most teams initially want. The common pattern that survives scrutiny is: all open transactions, current-year plus one prior year of closed transactions, and full master data. Older history belongs in a queryable archive or data warehouse, not the new ERP, because converting it inflates effort, slows every mock cycle, and pollutes the new system with legacy quirks. Regulated aerospace and defense manufacturers may need longer retention, but retention does not require conversion.
Why do mock conversions matter so much?
Because data migration defects are invisible until you load and reconcile at full volume. Each mock cycle surfaces mapping errors, encoding surprises, and business-rule gaps while there is still time to fix them cheaply. Projects that run three or more full mocks routinely cut over in a weekend without drama; projects that run one discover their defects during go-live week with the whole company watching. The 4 hours per entity per cycle this tool budgets is inexpensive insurance.
Get a specialist review of your data migration scope and see where accelerators can cut the estimate safely.
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