Data quality assessment for ERP migration

Data quality assessment for ERP migration determines whether source records are reliable enough to move into a new system. Profile critical fields, classify defects by business impact, and agree on measurable acceptance thresholds before cutover. Missing supplier identifiers may block migration, while optional contact gaps may be acceptable. Assign each exception an owner, verify corrections against source evidence, and obtain documented business approval before safely releasing migrated data into production.

Data quality assessment for ERP migration

A migration team discovers invalid supplier records two weeks before cutover. The profiling report lists hundreds of failures, yet nobody can say which defects threaten payments or which can be deferred.

The answer is to connect every finding to a business process, a defined record population, an approved acceptance rule, and a named decision-maker. A high pass rate cannot compensate for one unresolved defect that would misdirect a payment.

Use a three-stage decision flow: profile source fields → apply risk-based migration gates → resolve or formally reject exceptions. Recheck the target after each corrected load; source cleansing alone does not prove successful migration.

Field profiling

Field profiling measures the actual content of an ERP dataset before transformation. It exposes missing values, unexpected formats, duplicate identifiers, invalid references, and broken relationships between records.

Start with entities that support a real cutover decision. Supplier master data, payment instructions, legal-entity assignments, and open payables are more informative than a single enterprise-wide cleanliness percentage.

Profile the entire in-scope population where practical, then investigate representative records behind each failure. A sampling method should identify which populations it covers and which low-frequency defects it might miss.

Inputs and checks for field profiling

Profiling is defensible only when each result can be traced back to a dated extract, a field definition, and an agreed business rule.

Collect the following inputs before scoring readiness:

• Source scope: ERP instance, company code, entity, extraction time, inclusion rules, and record count.

• Field rules: mandatory values, permitted codes, unique identifiers, reference-table relationships, and conditional requirements.

• Business dependencies: which transactions or reports would fail if a field were wrong, blank, or mapped to another entity.

• Profiling evidence: failed-record IDs, executable checks, rule versions, and a reproducible result for each extract.

Distinguish a blank field from a genuine defect. An absent telephone number may be harmless, while a missing legal-entity assignment on an active payable may prevent correct posting.

Do not let profiling overwrite the evidence it evaluates. Store source snapshots and correction logs separately so reviewers can distinguish original defects from transformed values.

Once quality risks are defined, architects can assess which environment should store the working dataset and its verification history. For teams considering snowflake saudi arabia, the relevant question is whether the proposed design preserves source lineage, controlled access, and repeatable checks—not whether a platform can automatically approve source corrections.

Migration acceptance thresholds

An acceptance threshold states which defects are tolerable for a specified dataset and business operation. It must define the denominator, calculation, cutover consequence, and authority to grant an exception.

A generic target such as “98% data quality” can conceal severe failures. Instead, group rules by impact and assess each against the population that matters.

• Blocking controls: critical identifiers, posting references, and financial relationships that must pass before a dependent migration wave proceeds.

• Conditional controls: problems that can be temporarily accepted only with a documented workaround, exposure limit, owner, and review date.

• Nonblocking controls: low-impact attributes that may be repaired later without disrupting the defined cutover operation.

For each metric, state the numerator and denominator. “Valid active suppliers / all active suppliers in the migration wave” is actionable; “clean suppliers / all master records” may include dormant accounts irrelevant to the decision.

Decision or exception handling for migration acceptance thresholds

Finance, procurement, and data owners should approve what constitutes a blocking issue; migration engineers should calculate results and make failures visible.

An appropriate gate record includes the rule ID, eligible population, target value, actual result, risk category, evidence location, and signature of the accountable owner.

Thresholds should be versioned before rehearsal runs. Changing the definition after a failed test merely to raise the pass rate requires a separate, documented business decision.

When profiling scores are later used for management dashboards, the analytics team needs stable calculations and clearly marked exceptions. The wider capabilities described in data analytics services saudi arabia may support that monitoring, but the business still owns acceptance criteria and cutover authorization.

Exception resolution

Exception resolution is the controlled journey from an identified bad record to verified correction, approved deferral, or rejection. Closing a ticket is not the same as proving the record is safe to migrate.

Each exception needs an assigned business owner and a technical resolver. The issue record should show the original value, affected process, proposed treatment, target-state evidence, and final disposition.

For a critical error, correct the authoritative source when possible, rerun extraction and transformation, and then repeat the target checks. A manual edit to a staging file can disappear on the next load unless its treatment is governed.

Evidence and accountable approval for exception resolution

Use these states to keep handoffs unambiguous:

1. Open: the defect is reproducible and assigned to a business owner.

2. Remediated: a resolver has changed the source or applied an approved transformation.

3. Retested: the original rule and relevant downstream relationships pass using a new dated extract.

4. Approved or held: the accountable owner accepts the evidence or blocks the affected migration wave.

If procurement updates a supplier but finance cannot verify the associated payment instruction, the handoff has failed. Keep that supplier on hold; do not infer approval from the absence of another error message.

Migration teams may display exception aging, ownership, and retest status through reporting tools. For organizations evaluating microsoft power bi saudi arabia, a dashboard can surface the status, but its figures must trace to the controlled exception register and the owner's approval record.

Worked case and practical deliverable

Migration profiling finds invalid values but nobody knows which errors block cutover

Hypothetical example—not a TrustAngle customer project. A Saudi distributor plans to migrate 8,000 supplier records into a replacement ERP. The profiling run finds 36 active suppliers without an approved payment currency, 14 records with unmapped legal-entity codes, and 210 missing optional phone numbers.

The finance owner decides that the first two defects block activation of affected suppliers. The missing phone numbers are nonblocking for this migration wave because they are not used by the defined payment or posting workflows.

The figures and zero-tolerance conditions below are illustrative assumptions, not industry benchmarks or Saudi regulatory requirements.

The procurement steward corrects 31 of the 36 currency gaps. Five remain unsupported by source documents. The data engineer also resolves all 14 legal-entity mappings and reruns the checks.

The finance owner approves the corrected mappings but does not approve activation of the remaining five suppliers. The wave may proceed only if those records can be safely excluded without breaking open-payables dependencies; otherwise the dependent wave is delayed.

Owner RACI and case-specific acceptance tests

Assign decision rights before remediation begins. RACI means Responsible, Accountable, Consulted, and Informed.

• Profiling: data steward (R), data-domain owner (A), finance and migration architect (C), program sponsor (I).

• Correction: procurement operations (R), supplier-data owner (A), finance and integration engineer (C), migration lead (I).

• Cutover gate: migration lead (R), program sponsor (A), finance owner and technical operations (C), affected users (I).

Apply these tests to the stated example:

1. Input or condition: 14 supplier records contain unmapped legal entities. Expected result: mappings are approved and all 14 pass a new extraction, or affected records remain blocked. Evidence and owner: mapping register and target validation signed by finance.

2. Input or condition: five active suppliers still lack verified payment currency. Expected result: none can enter an enabled payment flow. Evidence and owner: supplier hold list, dependency check, and procurement owner's record.

3. Input or condition: 210 supplier phone numbers are blank. Expected result: the nonblocking classification is retained only if the approved migration scope does not depend on them. Evidence and owner: workflow inventory and supplier-data owner sign-off.

4. Input or condition: the corrected extract is loaded again. Expected result: migrated IDs, counts, legal-entity links, and payment controls match the approved baseline. Evidence and owner: reconciliation file and migration lead approval.

Failure case: an engineer patches the five currency values in the target without source approval, and the next extract overwrites them. The previous “pass” is invalid; the team must reopen the exception and repeat the cutover gate.

Data quality assessment for ERP migration — implementation checks and mistakes to avoid

Use a short, repeatable sequence across profiling rehearsals and the final migration window. Each stage should preserve the evidence used to authorize the next.

1. Freeze the population: identify the source snapshot, included entities, exclusions, and business processes.

2. Run approved rules: capture invalid fields, reference failures, duplicates, and dependent transactions.

3. Classify by consequence: separate cutover blockers from conditional and nonblocking findings.

4. Correct and retest: preserve original values and confirm the repaired data survives the full load.

5. Approve the gate: require a signed decision on remaining exceptions, affected populations, and fallback actions.

Data quality has broader dimensions than a migration pass rate, including completeness, consistency, validity, and uniqueness. TrustAngle's Data Quality Management: Six Dimensions and Who Owns Each One covers the continuing ownership model; this assessment focuses specifically on whether an ERP migration wave is safe to release.

Avoid accepting totals without checking relationships. A successful row count cannot prove that a supplier belongs to the correct legal entity or that an open payable references the right master record.

Do not confuse a successful technical import with operational readiness. Test critical business transactions, record any permitted exclusions, and keep a recoverable pre-cutover snapshot until authorized retirement procedures are complete.

FAQs about Data quality assessment for ERP migration

What inputs are needed for data quality assessment for erp migration?

Required inputs include source extracts, field dictionaries, target mappings, business rules, record populations, transaction dependencies, and ownership assignments. The migration lead must confirm the extraction date and scope; business owners should approve which fields are critical before testing starts.

How should field profiling be verified?

Reproduce the profiling checks on a dated source extract and inspect the record IDs behind major exceptions. Compare totals, formats, and relationships with the source application. A data steward documents the calculations, while the relevant domain owner validates business meaning and scope.

Who approves migration acceptance thresholds?

The business or data-domain owner approves thresholds according to operational impact, with finance approving finance-critical rules. Engineering supplies test evidence; it does not unilaterally accept financial risk. The approved threshold register must identify denominators, blocking conditions, permitted exceptions, and sign-off dates.

What happens if exception resolution fails?

Reopen or retain the exception, identify the unresolved dependency, and prevent affected records or waves from passing the cutover gate. The accountable owner may approve a controlled exclusion only after downstream impact is verified. Otherwise, delay the relevant migration activity and retest after correction.

Quality problems also affect later operational analytics. TrustAngle's Data-Driven Growth: Leveraging Analytics to Boost Saudi F&B Business Performance discusses a broader use of business data; it does not establish migration readiness for the supplier example.

Where executive reporting depends on disputed source values, request a bounded review of profiling evidence, approval rules, and reconciliation gaps before committing to a new dashboard design. TrustAngle's business intelligence consulting services saudi arabia is the planned advisory handoff for that reporting assessment, rather than a substitute for the ERP owner's cutover decision.