Epicor Kinetic Data Load Effort Calculator
Data migration is the part of an ERP project most often underestimated. This calculator turns record counts and error assumptions into a transparent hour estimate that you can challenge and refine.
Your numbers
Total across parts, partners, BOMs, balances and open documents.
Parts, customers, suppliers, BOM, open orders, balances and so on.
Your results
A planning model with simple assumptions. Real effort depends on data quality, tooling and team experience, so calibrate the inputs from your own test loads.
Total hours vs expected error rate
Sweeps Expected error rate first load from 0 to 40, holding your other inputs fixed.
Migration estimate review
Share your assumptions and we will give an independent view of the data migration effort.
Optional. The tool and every export work without this. No spam.
How to use it
Enter your record volume, the number of entities, the expected first-load error rate and your cost per hour. Adjust mapping hours per entity and fix effort based on how clean your data is. The result shows mapping, cleansing and test hours separately, plus the total and cost. Use the chart to see how sensitive the plan is to error rate.
- Count entities, not tables
- Use a test sample to measure the real error rate
- Recalculate after each test cycle
The model behind it
Total hours equals mapping plus cleansing plus testing. Mapping is entities times hours per entity. Cleansing is first-load errors divided by one hundred, times the fix effort per hundred errors. Testing is the number of cycles times hours per cycle. It is deliberately simple so assumptions can be read and argued about; it excludes training, cutover weekend time and project management.
Reading the results
Look at which component dominates. If cleansing is the largest, investing in profiling and de-duplication before the first load will pay off. If testing dominates, automate your reconciliation checks. The chart is usually steep: moving from a 5 percent to a 20 percent error rate can change total effort noticeably, which is why sampling early matters.
Common mistakes
Estimates fail when teams assume a clean source and never test. They also assume one test cycle, when three or more are typical for complex data. Treat the output as a starting range and revisit it once you have seen real errors.
- Assuming error rate is near zero
- Leaving out opening balance reconciliation
- Not budgeting for business-side data owners' time
Frequently Asked Questions
What does the error rate mean?
It is the share of records that fail validation or need manual correction in the first load. Measure it by loading a representative sample into a test environment rather than guessing.
Does it include go-live weekend effort?
No. It covers mapping, cleansing and test cycles. Plan cutover time separately using a cutover plan, since it depends on load volumes and reconciliation steps.
Which hourly rate should I use?
Use a blended rate across consultants and internal staff who do the work. If internal time is not charged, enter a lower rate and treat the output as external cost only.
How accurate is the estimate?
It is a planning range. Accuracy improves when you replace defaults with measurements from your own test loads, especially error rate, fix effort and hours per cycle.
If your estimate shows cleansing as the biggest cost, we can help with a private profiling and de-duplication assistant that runs on your own infrastructure.
Related Tools
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