Baan Data Migration Effort Calculator
This free calculator estimates the effort, cost, and timeline to migrate your data from Baan IV or Baan V into Infor LN, built for ERP program managers and IT directors scoping a migration. Data migration is the most underestimated workstream in Baan replacements - it routinely consumes 25-40% of total program effort. Enter your table scope, record volumes, data quality, custom-object count, and planned trial loads, and get an hours-based estimate broken into mapping, cleansing, and load-validation workstreams you can take straight into program planning.
Your numbers
Older estates carry more schema drift, orphaned records, and undocumented conventions.
Approximate row count across master and open transactional data you intend to migrate.
Distinct Baan tables to be mapped and converted, including custom tables.
Honest assessment of duplicates, dead records, and inconsistent conventions.
Custom tables and modified standard tables that need bespoke mapping decisions.
Full trial conversions before cutover. Three is the practical minimum for a clean go-live.
Your results
Estimates only, calibrated to typical Baan-to-LN conversion programs. Actual effort depends on profiling results, custom-table complexity, and cutover constraints.
Get your full Baan data migration effort report
We will email a personalized workstream-by-workstream effort breakdown with scope-reduction recommendations, and a data migration specialist will follow up to review it with you.
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How the effort model works
The calculator decomposes migration into the three workstreams every Baan-to-LN conversion actually runs. Mapping effort is driven by table count at roughly six hours per table for field-level mapping and transformation rules, scaled by data quality, plus 1.5 hours per custom object for bespoke decisions. Cleansing effort scales with record volume at about 25 hours per million records, plus 20 hours per year of estate age to account for accumulated schema drift and dead conventions. Load and validation effort is per iteration: each full trial load costs about 1.5 hours per table plus a fixed reconciliation overhead. A 15% buffer covers coordination and rework, and cost applies a $135 blended rate.
Benchmarks worth knowing before you scope
These planning benchmarks come from legacy Baan and Infor conversion programs in discrete manufacturing, and they explain why the calculator behaves the way it does:
- Data work consumes 25-40% of total Baan replacement program effort - more than most implementation quotes allocate
- Three full trial loads is the practical minimum; programs that cut to one trial load see 3-5x more cutover-weekend defects
- Poor data quality inflates mapping and cleansing effort by 40% or more, making profiling the highest-ROI early activity
- Migrating only open transactions and 2-3 years of history, with older data archived to a queryable store, typically cuts volume-driven effort by half
How to use your result
Treat the hours as a scoping midpoint and stress-test the two inputs that dominate it: data quality and table scope. If you have never profiled your data, assume Mixed at best - teams that self-report Clean without profiling are usually wrong. Then challenge scope: every table and every year of history you migrate costs mapping, cleansing, and validation hours, so a deliberate archiving strategy is the cheapest effort reduction available. Use the timeline output to check program dependencies - data migration must finish trial load two before integration testing starts, which often makes it the critical path rather than a background workstream.
How Netray compresses this effort
Netray attacks Baan data migration with AI-assisted tooling built specifically for legacy Infor estates. We profile your actual Baan tables automatically to replace quality guesses with measured defect rates, generate first-pass field mappings from the Baan and LN data dictionaries, and script repeatable conversion runs so each additional trial load costs a fraction of the first. On recent programs this approach has cut mapping and cleansing effort 30-50% against conventional estimates. We also design the archive strategy so you migrate what the business needs, not everything Baan accumulated since the 1990s.
Frequently Asked Questions
How much Baan history should we migrate into Infor LN?
Most successful programs migrate all master data, all open transactions, and two to three years of closed transactional history, then archive the rest into a queryable read-only store. Migrating 15-plus years of closed transactions multiplies cleansing and validation effort while delivering little operational value - finance and compliance needs are almost always better served by an archive. Cutting history scope is typically the single largest effort reduction available, often 40-50% of volume-driven hours.
Why are multiple trial loads necessary?
Because every trial load surfaces defects you cannot find any other way: mapping errors, encoding issues, referential breaks, and reconciliation gaps between Baan balances and LN. The first load typically fails reconciliation badly, the second proves your fixes, and the third rehearses cutover timing under realistic conditions. Programs that skip to a single load routinely discover critical defects during the cutover weekend itself, when the cost of a defect is measured in go-live delay, not hours.
Can data migration start before the LN design is finished?
Partially, and it should. Data profiling, cleansing of obvious defects, and duplicate resolution are design-independent and can start on day one - they are also the longest-lead activities. Field-level mapping needs the LN configuration to be stable for the target structures involved, so it follows design by module. Running profiling and cleansing early is how well-run programs keep data off the critical path; leaving all data work until after design is the classic way it becomes the bottleneck.
Get your effort estimate, then let Netray replace the assumptions with a real data profile of your Baan environment.
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