How do you calculate an ERP data readiness risk score?

Multiply severity by occurrence by detection on a common scale. With severity 6, occurrence 4 and detection 3 the underlying product is 72, normalized here to a 4.55 readiness risk score for ranking.

What is a high ERP data readiness score?

High scores flag the fields to fix first. Like an FMEA RPN, anything driven by a high severity combined with poor detection should jump the queue regardless of how it ranks against a fixed threshold.

What does each of the three factors mean?

Severity is the damage if the field is wrong at go-live, occurrence is how often it's wrong in the current data, and detection is whether your validation catches it before cutover — a low detection score means it's likely to slip through.

Why multiply instead of add the three scores?

Multiplication makes a single extreme factor dominate. A field that is catastrophic but rarely caught scores far higher than three middling factors, which is exactly the prioritization you want before a cutover.

ERP readiness score vs a simple defect count — why bother?

A defect count treats every problem as equal. The readiness score weights by impact and catchability, so you fix the few defects that will break go-live instead of burning time on cosmetic ones.

How often should we re-score during a project?

Re-score at each data-readiness checkpoint. As cleansing improves occurrence and new validations improve detection, scores fall — letting you prove readiness is actually trending toward green.

Manufacturing Master Data & Data Governance calculator

ERP Data Readiness Score Calculator

ERP Data Readiness Score applies FMEA logic to migration risk, multiplying the business impact of a bad field, how often it's wrong today, and how likely the error is to slip through to go-live. ERP project managers, data stewards and cutover risk owners use it to rank which master-data gaps must be fixed before launch versus which can wait. It matters because every migration has more known data problems than time to fix them, and gut-feel prioritization buries the few errors that will actually break order-to-cash. A single multiplied risk score turns a sprawling defect list into a ranked remediation backlog.

What this calculator does

Estimate ERP data readiness for manufacturing master data and data governance using production-ready inputs so teams can rank risks and decide which issue needs containment, controls, or escalation first.
Use it when erp data readiness in manufacturing master data and data governance needs a defensible ranking against other manufacturing master data and data governance risks for the next review.
It multiplies severity, occurrence and detection scores into a single ERP data readiness risk number for ranking master-data defects before go-live.

Formula used

ERP data readiness risk score = ERP data readiness severity score × ERP data readiness occurrence score × ERP data readiness detection score
Use the same scoring scale across comparable ERP data readiness risks.

Inputs explained

Business impact if the field is wrong:
How often the field is wrong today:
Chance the error is caught before go-live:

How to use the result

Use it during data-readiness reviews and cutover gating to prioritize which fields and objects to remediate first.
It is a relative ranking tool, not an absolute risk measure; scores are only comparable when every reviewer uses the same defined scale for all three factors.

Common questions

How do you calculate an ERP data readiness risk score? Multiply severity by occurrence by detection on a common scale. With severity 6, occurrence 4 and detection 3 the underlying product is 72, normalized here to a 4.55 readiness risk score for ranking.
What is a high ERP data readiness score? High scores flag the fields to fix first. Like an FMEA RPN, anything driven by a high severity combined with poor detection should jump the queue regardless of how it ranks against a fixed threshold.
What does each of the three factors mean? Severity is the damage if the field is wrong at go-live, occurrence is how often it's wrong in the current data, and detection is whether your validation catches it before cutover — a low detection score means it's likely to slip through.
Why multiply instead of add the three scores? Multiplication makes a single extreme factor dominate. A field that is catastrophic but rarely caught scores far higher than three middling factors, which is exactly the prioritization you want before a cutover.
ERP readiness score vs a simple defect count — why bother? A defect count treats every problem as equal. The readiness score weights by impact and catchability, so you fix the few defects that will break go-live instead of burning time on cosmetic ones.

Last reviewed 2026-05-12.