Key Takeaways
- UAT data quality issues are usually the real reason a testing cycle repeats, even when the defect list seems like a workflow problem.
- Missing prices and mismatched item codes are the most visible signs of trouble, and they routinely surface during testing.
- Teams that keep asking “why does ERP testing keep stalling?” are often looking at a data problem rather than a workflow problem.
- A structured approach to ERP data cleanup before go-live restores confidence in the go-live date faster than another round of scripted test cases.
User acceptance testing (UAT) is the final checkpoint before an ERP system goes live, the stage where the people who will use the software confirm it can handle real transactions. Unfortunately, data problems often stall the process, requiring last-minute ERP data cleanup before go-live.
Today, we are exploring UAT data quality issues and why so many testing cycles stall on data instead of design.
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What UAT Data Quality Issues Reveal About Go-Live Readiness
User acceptance testing is a check on workflow design, but it only does that job well when the records underneath it are sound.
The trouble starts when a tester opens a real product record to process a transaction and finds a price field that was never populated in the legacy system. Sometimes a record carries an item code that was renumbered during a prior software change and never reconciled.
Regardless of the type of data issue, the key to prevention is working with an ERP consulting partner that prioritizes data readiness during selection and implementation.
Expert Insight
Our ERP implementation team has found that organizations who assign a named data owner before configuration begins resolve UAT defects in roughly half the cycles of those who wait for testing to surface them.
Why Does ERP Testing Usually Stall?
Project teams often describe the same pattern. A test cycle is scheduled, and a defect list comes back a week later. The team spends that week on fixes, and the next cycle produces a new list of defects that look nothing like the first one. That pattern is the clearest sign that the underlying issue is data rather than design.
Four issues tend to account for most of the delay:
- Missing or placeholder pricing - Legacy items that were rarely sold or manually priced at the point of sale often have no price field populated in the source system, so the new ERP has nothing to migrate.
- Mismatched item and vendor codes - Numbering schemes that were reconciled manually for years in the old system break the moment a purchase order or invoice tries to match them automatically.
- Uneditable legacy records - Permissions inherited from the prior system prevent the very people who could fix a record from opening it.
- No named owner for the fix - A defect gets logged, but no one on the project has explicit authority to correct master data, so it sits until the next test cycle exposes it again.
For example, a pharmaceutical company testing whether its new system can process a routine purchase order might find that a third of its active item records have no current price. At the same time, several vendor codes might have been renumbered since the legacy system went live. Together, these issues could stall go-live for multiple weeks.
This pattern shows up so often in ERP implementations and supply chain management software deployments that we have documented practical fixes here: ERP data migration and cleansing tips.
The Business Cost of Stalled Testing and a Slipping Go-Live Date
Gartner has estimated that poor data quality costs the average organization $12.9 million a year, and an ERP go-live is where that cost concentrates into a single, highly visible test cycle.
Executives increasingly want visibility into how many records still need attention before they will sign off on that date. That shift in confidence is often more damaging than the delay itself. A steering committee that stops trusting the schedule starts adding review gates that slow every subsequent phase.
Many organizations respond by bringing in an outside ERP consultant to assess data readiness once testing has stalled more than once, since the internal team that built the migration scripts is rarely positioned to audit its own output objectively. The earlier an outside read happens, the smaller the schedule impact tends to be.
ERP Data Cleanup Before Go-Live: A Practical Sequence
Closing UAT data quality issues before they reach a test script is faster and less disruptive than fixing them mid-cycle. The sequence below assumes a project that has not yet reached UAT.
- Audit Master Data Before Configuration Begins - Pull a full extract of master data records before configuration starts, and flag every record missing a required field, such as price or unit of measure. This audit belongs in the project charter rather than in a phase that starts after testing has already stalled.
- Assign a Named Data Owner for Every Object Type - Name a single accountable owner for each master data object type, someone who can correct records directly inside the legacy system while it is still editable. Waiting until legacy access is locked down removes the only window in which some of these records can still be fixed at the source.
- Run a Mock UAT Cycle Against Real Records - Before the scheduled test cycle, run a short mock cycle using a sample of real production records rather than clean demo data. This surfaces data defects on a low-stakes calendar instead of the one leadership is watching.
- Build a Remediation Buffer Into the Project Schedule - Treat data cleanup as its own line item with its own duration estimate, rather than folding it into general configuration time. A project that has budgeted time for this work rarely needs to renegotiate the go-live date when the first defect list comes back long.
Learn More About ERP Data Cleanup
Stalled UAT is often a sign that master data was never audited with the same rigor as the workflow design around it. Closing that gap before testing begins protects both the go-live date and the confidence of everyone watching it.
Panorama's ERP implementation services team builds data readiness into the project schedule from day one rather than treating it as a testing-phase surprise. Contact us below to learn more.
FAQs About UAT Data Quality Issues
Why does ERP testing sometimes stall even after the project team fixes each defect?
Because most fixes address one record at a time instead of the master data audit that would catch the pattern behind them. Each test cycle finds a new example of the same underlying problem, so the defect list never actually shrinks until the data is reviewed as a whole.
What are the most common UAT data quality issues teams encounter?
The most frequent issues are missing or placeholder pricing on legacy items, together with item codes that do not match between the old and new systems. Uneditable legacy records add a third layer, since permissions from the prior system often prevent testers from correcting what they find in real time.
How is ERP data cleanup before go-live different from data migration?
Data migration moves records from one system to another. ERP data cleanup before go-live is the review that happens first, confirming those records are complete and accurate enough to migrate correctly rather than simply carrying existing errors into a new environment.
When should data cleanup start relative to UAT?
Ideally before configuration begins, rather than during the first test cycle. Organizations that wait for UAT to reveal data problems lose the option of correcting many records at the source, since legacy system access is often restricted once the new system goes live.
What happens if a stalled UAT cycle is overridden and the go-live date holds anyway?
The unresolved records surface again during hypercare, often at a moment when the operational team is already managing the stress of a live cutover, which turns a testing-phase fix into a production-support fire drill.
