Evidence-led field guide
ERP data migration plan and reconciliation guide
Plan ERP data migration through ownership, profiling, mapping, cleansing, rehearsal, access controls, reconciliation, cutover, and retained evidence.
Data migration is a controlled change to operational truth. Moving files is only one step. The plan must decide what data is authoritative, who may correct it, how old meanings map to the new model, what will not move, how balances and counts reconcile, and what evidence remains after cutover.
What to define
Inventory source systems, owners, formats, volumes, quality issues, sensitive fields, retention needs, and dependencies. Define mapping and cleansing rules with the business owner of each record. Separate master data, open transactions, balances, history, documents, and reference tables because each class has different acceptance and rollback needs.
A practical review sequence
- Profile source data before promising a migration date.
- Version mapping and cleansing rules with named owners.
- Run at least one representative rehearsal and reconcile results.
- Restrict extracts, temporary files, imports, and correction authority.
Evidence to retain
Retain source counts and hashes where appropriate, mapping versions, transformation logs, rejected rows, approvals, import results, reconciliation reports, access records, and disposal decisions for temporary data. Acceptance should test meaning and usability, not only row count, and should show how an error can be traced and corrected safely.
Truth and scope boundary
This guide is educational and does not guarantee complete, lossless, fast, or automatic migration. It does not establish that every source format, attachment, history type, or integration is supported by Balaawi. Exact tooling, scope, privacy duties, downtime, and reconciliation require discovery and acceptance.
A responsible next step
Profile one representative dataset and produce a draft mapping plus reconciliation rule. Use the findings to estimate work and risk before expanding the promised migration scope.
Questions teams ask next
What should an operating team understand about Data ownership?
data ownership assigns accountability for meaning, quality, access, retention, correction, and acceptable use of each important record set. The practical scope should name data domain, authoritative source, owner, steward, consumers, quality rules, sensitivity, retention, correction path, and export needs, so the term leads to a testable operating decision rather than a broad label.
When should a team review Data ownership?
Review Data ownership when ownership, volume, risk, locations, language, data, or decision needs change. Start with the affected workflow and evidence, then decide whether process, configuration, training, or another control must change.
What is the first practical step for Data ownership?
Write one current workflow from trigger to closure, including data domain, authoritative source, owner, steward, consumers, quality rules, sensitivity, retention, correction path, and export needs. Mark what is authoritative, who decides each state change, and which exception currently consumes the most attention before discussing software changes.
Which records should be defined for Data ownership?
At minimum, define data domain, authoritative source, owner, steward, consumers, quality rules, sensitivity, retention, correction path, and export needs. For each record, state its identifier, owner, lifecycle, required evidence, sensitivity, correction path, retention need, and the report or decision that consumes it.
Source register
References used to bound this guide. External sources open in a new tab.
- Cybersecurity Framework 2.0National Institute of Standards and Technology
- Role Based Access ControlNational Institute of Standards and Technology
Evidence standard: Source-governed educational record
Plan one bounded review