SAP Migration · 5 min read · May 2026
The true cost of dirty data in an SAP migration
By Thinklytics Partners, SAP S/4HANA Practice
The cost of a data problem rises sharply the later it is found. A duplicate cleaned during planning is routine. The same one found after go-live is an incident.
The cost of a data problem rises sharply the later it is found. A duplicate cleaned during planning is routine. The same duplicate found at conversion is a schedule risk, and after go-live it is a business incident.
What a data defect costs by the time you find it
The same duplicate, three points in the program. The cost multiplies as you move right.
| When you find it | What it becomes | Relative cost |
|---|---|---|
| During planning | A routine cleanup task | 1x |
| During conversion | A schedule risk and rework | 10x |
| After go-live | A business incident: finance cannot reconcile | 100x |
Source: Thinklytics SAP data readiness practice, 2026.
The cost escalation
Fix a data problem during planning and it is a task. Find it during conversion and it is a schedule risk. Discover it after go-live, when finance cannot reconcile, and it is an incident with real business cost. The same defect costs roughly ten times more at conversion and a hundred times more in production. The economics strongly favor early.
The numbers
Best practice puts 25 to 30 percent of migration effort into data prep precisely because skipping it causes rework and delay. Programs that skip it run about 30 percent longer and 30 to 50 percent over budget, while pre-move cleansing cuts post-migration defects by roughly 60 percent. The most expensive line item in any migration is rework discovered at cutover.
- 40% of the migration timeline is discovery and data cleansing. Pre-move cleansing with validation rules cuts post-migration defects by about 60% and post-go-live performance issues by about 25%. The work you skip up front returns as production incidents.
The upside
Clean data does more than avoid cost. It produces a digital core that is actually ready for modern analytics and AI, which is the reason most companies are modernizing in the first place. The data work pays for itself twice: in avoided overruns, and in a foundation worth building on. The 30-day Analytics Truth Audit tells you how much dirty data you are about to pay for.
Frequently asked questions
What does dirty data cost in a migration?
More the later it is found. A duplicate cleaned in planning is a task, at conversion a schedule risk, and after go-live a business incident when finance cannot reconcile.
How much does skipping data prep cost?
Programs that skip it run about 30 percent longer and 30 to 50 percent over budget. The most expensive line item in any migration is rework discovered at cutover.
How much effort should go to data prep?
Best practice puts 25 to 30 percent of total migration effort into data preparation and validation, precisely because skipping it causes rework.
Is there an upside to clean data beyond avoiding cost?
Yes. Clean, governed data produces a digital core ready for modern analytics and AI, which is the reason most companies are modernizing in the first place.
When is the cheapest time to fix data?
During planning, before you commit to a date. The same fix gets dramatically more expensive at conversion and after go-live.
How do we control the cost?
Run a readiness assessment first, front-load remediation where it is cheap, and keep the data workstream senior so problems are caught early.
Topics covered
- Data Quality
- Cost
- Migration
- S/4HANA
Frequently asked questions
What does dirty data cost in a migration?
More the later it is found. A duplicate cleaned in planning is a task, at conversion a schedule risk, and after go-live a business incident when finance cannot reconcile.
How much does skipping data prep cost?
Programs that skip it run about 30 percent longer and 30 to 50 percent over budget. The most expensive line item in any migration is rework discovered at cutover.
How much effort should go to data prep?
Best practice puts 25 to 30 percent of total migration effort into data preparation and validation, precisely because skipping it causes rework.
Is there an upside to clean data beyond avoiding cost?
Yes. Clean, governed data produces a digital core ready for modern analytics and AI, which is the reason most companies are modernizing in the first place.
When is the cheapest time to fix data?
During planning, before you commit to a date. The same fix gets dramatically more expensive at conversion and after go-live.
How do we control the cost?
Run a readiness assessment first, front-load remediation where it is cheap, and keep the data workstream senior so problems are caught early.