ERP Data Migration Cost (2026): How Much It Costs to Move from Legacy Systems (Plus Checklist)

What ERP Data Migration Cost Includes (and What It Doesn’t)

When vendors quote “data migration,” they often mean different things. To compare proposals and avoid overpaying,
you need a clear definition of what migration work actually includes.

Typically included in a complete migration scope

• Data discovery & profiling: inventory of sources, field completeness, duplicates, invalid values, outliers.
• Migration scope definition: what moves vs what stays (master data, open transactions, history, attachments).
• Field mapping & transformation rules: mapping legacy fields to ERP fields; unit/currency conversions; code translations.
• Data cleansing strategy: deduplication, standardization, validation rules, and ownership of cleanup work.
• ETL build: extract-transform-load scripts, templates, staging tables, and/or migration tooling setup.
• Trial loads (multiple cycles): test loads in sandbox/UAT environments, then corrections and repeats.
• Validation & reconciliation: totals match, counts match, balances match, and business rules pass.
• Cutover load: final pre-go-live data freeze, last extraction, final load, and sign-off.
• Hypercare fixes: post go-live corrections, missing mappings, and urgent data repairs.

Often NOT included unless you force it into scope

• Business-owned cleansing time: stakeholder hours to fix duplicates, merge customers, normalize item masters.
• Historical reporting rebuild: replicating “10 years of reports” from a legacy system inside the new ERP.
• Document/attachment migration: PDFs, invoices, quality records, engineering drawings.
• Integration-driven migration: ongoing sync/CDC (change data capture) during a phased rollout.
• Data governance setup: master data ownership model and long-term data quality monitoring.

Recommendation: Require every bidder to list migration objects, number of cycles, validation approach, and exactly what “history” means—otherwise you’ll compare quotes that aren’t comparable.

Common Cost Benchmarks (Percent of ERP Project Budget)

The most useful way to benchmark ERP data migration cost is as a percentage of the total implementation budget.
In many real projects, migration is not “a small add-on.” It can be a major workstream because it involves multiple cycles, coordination across teams, and rigorous reconciliation.

• Planning benchmark: data migration may add around 10–15% of the overall cost of an ERP project, even when moving from an older ERP to a newer one.
• Broader project reality: some teams observe data migration consuming roughly 15–25% of the implementation budget, especially when multiple cycles are required and data quality is inconsistent.

These benchmarks don’t mean you should “spend 15%.” They mean you should budget migration as a serious workstream with real effort:
discovery, cleansing, multiple loads, testing, and reconciliation. If your quote says “migration included” without defining cycles and validation, treat that as a risk—not a discount.

The 12 Biggest Cost Drivers When Moving from Legacy Systems

ERP migration cost is driven by complexity, not just data volume. Two companies with the same number of records can have radically different costs depending on how messy and how interconnected the data is.

1) Number of source systems

Migrating from one legacy ERP is simpler than consolidating three ERPs plus spreadsheets plus a custom Access database.
Each source introduces mapping rules, duplicates, and reconciliation work.

2) Data quality and consistency

Duplicates, missing keys, inconsistent naming conventions, and “free-text fields” add labor. Every bad record becomes a decision:
fix it, merge it, ignore it, or rebuild it.

3) The object list (what you’re migrating)

The scope expands quickly once you include manufacturing and supply chain objects: BOMs, routings, work centers, quality plans, lot/serial rules, customer-specific pricing, and complex tax mappings.

4) Open transactions vs history

Migrating open AP/AR, open POs/SOs, inventory balances, and open WIP is usually required. Migrating years of historical transactions is optional—but often requested.
History adds volume, testing effort, performance considerations, and reporting rebuild effort.

5) Transformations and business rules

Converting units of measure, merging account structures, redesigning item codes, changing tax logic, and remapping warehouses all add complexity.
“Same fields, same meaning” is rare in ERP replacements.

6) Required validation and reconciliation depth

Finance-grade reconciliations (trial balance, AR/AP aging, inventory valuation, WIP) require more time than “record counts match.”
The more regulated your environment, the more proof you need.

7) Number of migration cycles

You rarely migrate once. A typical program runs several mock loads: early prototype load, UAT load, regression load, and final cutover load.
Each cycle finds new issues, which require fixes, then reruns.

8) Cutover approach (big bang vs phased)

Big bang cutovers require a strict freeze window and a clean final load. Phased rollouts may require interim sync, dual-running, and “bridge” integrations that keep systems aligned during transition.

9) Attachments, documents, and unstructured data

Many small businesses forget this until late: invoices, contracts, QA certificates, SDS documents, and drawings often live outside the ERP.
Migrating them can be costly depending on indexing and linkage requirements.

10) Customizations in the legacy system

If your old system contains custom fields, custom workflows, and custom tables, you either replicate those customizations (costly) or redesign processes (also costly, but often smarter long-term).

11) Performance and load window constraints

If you only have a weekend to cut over, you need optimized loads, staging, rehearsal, and rollback planning. Tight windows raise cost.

12) Governance: decisions, ownership, and change control

Projects get expensive when nobody owns master data decisions. If definitions change weekly (“What is a customer?” “What is an active item?”), mappings churn and cycles multiply.

How Migration Services Are Priced (T&M vs Fixed-Fee vs Per Object)

You’ll see three common pricing models for ERP data migration services in 2026. The best model depends on how well you know your data.

1) Time & Materials (T&M)

You pay for hours worked (analysts, developers, testers). This is common when data quality is uncertain because it’s hard to predict cleansing and reconciliation effort.
If you choose T&M, protect yourself with a weekly burn cap, defined deliverables, and a cycle plan.

2) Fixed-fee by phase

Vendors quote discovery + mapping + build + trial loads + cutover + hypercare. This can be excellent for predictability,
but only if the scope is tightly defined (objects, cycles, validation depth, and what “history” includes).

3) Fixed-fee by data object (complexity tiers)

Pricing per object (e.g., “customers,” “items,” “open AR/AP,” “BOMs,” “inventory balances”) with tiers such as simple/moderate/complex.
This often creates the cleanest quote comparison—because it forces transparency on what’s included.

Reality check: You still pay for business participation. Even with fixed-fee vendors, internal time to cleanse and validate is a major cost.
Budget for it explicitly, or it becomes the invisible reason your timeline slips.

A Practical Estimator: Objects × Complexity × Cycles

To estimate ERP data migration cost without guessing, build your forecast around three measurable elements:
(1) migration objects, (2) complexity level, and (3) number of cycles.
Then layer in validation depth and cutover constraints.

Step 1: List your migration objects

At minimum, most ERP replacements migrate:

• Customers
• Vendors
• Items/SKUs (including units, cost methods, and statuses)
• Chart of accounts + opening balances
• Inventory on-hand balances
• Open AR/AP
• Open purchase orders and sales orders

Manufacturing adds: BOMs, routings, work centers, WIP, quality specs, lot/serial rules, planning parameters, and sometimes engineering change data.

Step 2: Assign complexity (Simple / Moderate / Complex)

Use a repeatable rule:

• Simple: mostly clean, few transformations, few dependencies, low exception risk.
• Moderate: some cleansing, multiple dependencies, transformations, and validation beyond counts.
• Complex: major redesign, multiple sources, heavy exceptions, strict audit controls, or multi-entity rules.

Step 3: Multiply by cycles

Costs increase with each mock load. A practical plan often includes:
prototype load → UAT load → regression load → cutover load.
If you have many integrations or business-rule changes, assume more cycles.

Illustrative hours-per-object model (use for budgeting)

The table below is an estimator framework you can adjust based on your environment and your team’s rate card.
It’s designed to help you compare vendor proposals and identify “under-scoped” migration quotes.

How to use: estimate hours per object → multiply by blended hourly rate → add tool costs (if any) → add contingency (10–25%).
Then compare against vendor quotes. If a vendor proposes 1–2 cycles for a messy legacy system, ask how they’re managing risk.

Realistic Examples: What Migration Costs Look Like in Practice

The following examples are illustrative planning scenarios—not universal price quotes. They show how cost grows with complexity, not just company size.

Example A: Small business moving from spreadsheets + accounting software to a cloud ERP

• Sources: spreadsheets + basic accounting tool
• Objects: customers, vendors, items, opening balances, on-hand inventory, open AR/AP
• Complexity: moderate (data inconsistent, duplicates exist)
• Cycles: 3 (prototype, UAT, cutover)

Cost is often dominated by cleansing (standardizing customer names, addresses, tax rules, item UOMs) and deciding what “truth” is.
The migration work may be manageable, but stakeholder time is significant.

Example B: Mid-market distribution company replacing a legacy ERP with WMS + eCommerce integrations

• Sources: legacy ERP + WMS + eCommerce + CRM
• Objects: all core objects plus price lists, multi-warehouse bins, shipping rules
• Complexity: moderate-to-complex (multiple systems, exception-heavy processes)
• Cycles: 4–5 (prototype, UAT, regression, cutover, post-go-live fixes)

Here, migration cost rises because reconciliation isn’t just “counts.” You must validate inventory valuation, allocations, open orders accuracy, and operational continuity across integrated systems.

Example C: Manufacturer migrating from a customized legacy ERP with BOMs, routings, and WIP

• Sources: customized legacy ERP + spreadsheets for engineering changes
• Objects: BOMs, routings, planning parameters, work centers, open WIP, quality specs, lot/serial rules
• Complexity: complex (deep dependencies and process rules)
• Cycles: 4–6 with strict cutover rehearsal

Manufacturers often face the highest migration effort because data is connected: changing an item’s UOM impacts BOMs,
costing, inventory, purchasing, and production reporting. Testing needs to simulate real operational edge cases.

Hidden Costs That Cause Overruns (and How to Block Them)

Most ERP migration overruns come from predictable “hidden” items. If you handle them early, your migration budget becomes much more stable.

1) Business time is not free

Data owners must decide: merge duplicates, retire inactive customers, standardize item masters, confirm tax categories, and approve mapping.
If stakeholders are unavailable, cycles repeat and consulting hours rise.

2) “History” requests balloon scope

Users often ask for “all historical transactions” because they’re afraid of losing reporting. A cheaper strategy is:
migrate operational history you truly need (e.g., open + recent), then archive the rest in a reporting warehouse or read-only legacy access.

3) Attachments and unstructured documents

If you need searchable attachments inside the ERP (with links, metadata, and permissions), scope it explicitly.
Otherwise it becomes a late-stage surprise.

4) Under-scoped validation

If validation is limited to “row counts match,” errors will surface after go-live. Finance-grade reconciliation needs totals, aging, valuation,
and exception reporting.

5) Not budgeting for multiple cycles

Multiple cycles are normal. If your vendor quote assumes one test load plus cutover load, clarify what happens when issues appear in UAT.
The best migration plans assume repetition and bake it into schedule and budget.

ERP Data Migration Checklist (Copy/Paste)

Use this checklist to control scope, timing, and cost. It’s designed for ERP migrations from legacy systems and includes both technical and business tasks.
Copy it into your project plan and assign an owner to each line item.

Phase 1 — Plan & Assess

• ☐ Define migration goals (what must be accurate on day 1?)
• ☐ Inventory all data sources (legacy ERP, spreadsheets, CRM, WMS, custom DBs)
• ☐ Decide what data will migrate (master, open transactions, history, attachments)
• ☐ Assign data owners for each domain (customers, items, vendors, finance, inventory, BOMs)
• ☐ Profile data quality (duplicates, missing fields, invalid values, outliers)
• ☐ Define transformation rules (UOM, currencies, codes, statuses, naming conventions)
• ☐ Define validation rules and reconciliation reports (counts + totals + balances)
• ☐ Choose tools (ERP import templates, staging tables, ETL/iPaaS as needed)
• ☐ Plan number of cycles (prototype, UAT, regression, cutover, hypercare fixes)
• ☐ Define cutover approach and freeze window (weekend, phased rollout, parallel run)

Phase 2 — Map, Clean, Build

• ☐ Create field mapping documents for each migration object
• ☐ Build transformation logic and staging structure
• ☐ Clean data (deduplicate, standardize addresses, normalize item masters)
• ☐ Build exception reports (what fails validation, why, and who fixes it)
• ☐ Document data governance rules for the new ERP (who maintains what going forward)

Phase 3 — Trial Loads (Repeatable Cycles)

• ☐ Run prototype load (early test) and capture issues
• ☐ Fix mappings/transformations and re-run as needed
• ☐ Run UAT load and complete end-to-end process testing
• ☐ Perform reconciliations (trial balance, AR/AP aging, inventory valuation, WIP if applicable)
• ☐ Run regression load after configuration changes or integration updates
• ☐ Confirm performance and load times meet cutover window limits

Phase 4 — Cutover & Go-Live

• ☐ Final data freeze and last extraction timing approved
• ☐ Execute final cutover load (master + open transactions + balances)
• ☐ Execute reconciliation sign-offs (finance + operations)
• ☐ Confirm user access and roles for go-live
• ☐ Confirm rollback plan and decision criteria

Phase 5 — Post Go-Live Stabilization

• ☐ Track data defects and resolve quickly (hypercare)
• ☐ Monitor master data creation standards to prevent “new garbage”
• ☐ Finalize documentation and knowledge transfer
• ☐ Close out legacy system access strategy (read-only archive, reporting warehouse, retention plan)

FAQ

How much does it cost to migrate data from a legacy ERP to a new ERP?

It depends on your object scope, data quality, number of sources, and validation requirements. A useful planning benchmark is that data migration can represent a meaningful portion of total ERP project cost. The safest approach is to estimate by objects, complexity, and number of cycles—not by record count alone.

Why do ERP data migrations need multiple cycles?

Because each trial load reveals issues: missing mappings, invalid codes, duplicate masters, and edge-case business rules. Those issues must be fixed and tested again. Multiple cycles also support UAT, regression, and cutover rehearsal.

Should we migrate historical transactions?

Only if there is a clear operational or compliance requirement. Many teams migrate open transactions and recent history, then keep older history in a reporting warehouse or read-only legacy access. This reduces cost, improves performance, and speeds go-live.

What’s the biggest mistake that increases migration cost?

Not assigning data owners. When nobody owns master data decisions, mappings change, cycles repeat, and validation becomes endless. Strong data ownership is the fastest path to a predictable migration budget.