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Data Cleansing Batch Effort Calculator

Cleansing effort is dominated by how many records you can fix with rules and how many a human must touch. This calculator makes that trade-off visible, giving hours, weeks and labor cost for a cleansing batch and showing how each extra point of automation pays back.

Your numbers

Records in scope

records

Fixed by rules or scripts

60 %

Share resolved automatically: case, whitespace, unit mapping, standard abbreviations, defaults.

Manual minutes per remaining record

min

Validation sample of all records

Share of all records checked by a second person after cleansing.

Minutes per validated record

min

People working on cleansing

people

Productive hours per person per day

hours

Loaded hourly rate

$/hour

Your results

Total effort

3,400 hrs

Calendar duration

28.3 weeks

Records needing manual work

48,000

Manual cleansing hours

3,200 hrs

Validation hours

200 hrs

Person-days

566.6667

Labor cost

$136,000

Estimates exclude project management, rule development and tool licences. Add those separately.

Save results:

Total effort hours vs share fixed automatically

Sweeps Fixed by rules or scripts from 0 to 95, holding your other inputs fixed.

Cleansing plan review

Optionally have an expert sanity-check your automated fix share and effort estimate against your data profile.

Optional. The tool and every export work without this. No spam.

How to use it

Enter the records in scope for the migration or cleanup wave. Estimate the automated share by profiling a sample: run rules for case, trimming, unit of measure mapping and standard descriptions, and count what is still failing. Manual minutes should reflect investigation such as looking up a supplier catalog, not just typing. Validation is a second-person check on a sample of the cleansed output, which is the control that tells you whether the first pass was good. Team size and productive hours turn effort into calendar weeks, assuming a five-day week.

The method and what the chart shows

Manual hours equal records times one minus the automated share, times minutes per record, divided by sixty. Validation hours are records times the sample share times validation minutes, divided by sixty. Total effort is their sum, person-days divide by productive hours, and calendar weeks divide person-days by team size times five. The what-if chart sweeps the automated share from zero to 95 percent. Effort falls steeply at first, then flattens toward the fixed validation floor, which shows where building more rules stops being worth it.

Manual hours = records x (1 - auto %) x minutes / 60
Validation hours = records x sample % x minutes / 60
Weeks = person-days / (people x 5)

Common mistakes

Teams often assume a high automated share before they have profiled the data, then discover that free-text descriptions and missing attributes resist rules. Another trap is excluding the validation sample to hit a date, which pushes defects into the new ERP where they cost many times more to fix. Also remember that cleansing is iterative: load rehearsals reveal new defect classes, so budget two or three passes rather than one. Finally, subject matter experts such as engineers are scarce, so check their availability separately from the headcount used here.

Frequently Asked Questions

What is a realistic automated fix share?

It depends on how structured the source is. Formatting and mapping defects automate well, often covering half of all findings or more. Missing technical attributes and ambiguous descriptions usually need a human or a cross-reference source.

How large should the validation sample be?

Use the master data audit sample size calculator to choose a statistically sound figure. For quick planning, five percent of cleansed records is common, with a higher rate for critical fields.

Does this include rule development time?

No. Building and testing scripts, mappings and match rules is separate project effort. Treat it as an upfront cost that moves the automated share up and the manual hours down.

Can I use it for vendor and customer data?

Yes. Adjust manual minutes upward for records needing external verification, such as tax IDs or bank details, and add a compliance review step where regulations apply.

Where manual minutes are spent on lookups and normalizing free text, language models running on your own hardware can propose fixes for a person to accept. Netray can scope that against your actual data profile.

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