Services / Data cleanup & migration
An integration or new platform is only useful when the right information reaches it. Automate Axis prepares and migrates business data with explicit field mappings, matching rules and reconciliation checks. We work with the people who understand the records to distinguish a formatting problem from a decision that needs an owner. The work can support a CRM move, a spreadsheet-to-database project, a reporting programme or a connection between existing systems. We define the source, destination and cutover conditions before implementation so your team understands what is moving, what is being corrected and what remains for review.
A /
A field map your team can review
Example
A REVIEWABLE MAPPING SPECIFICATION
SOURCE FIELD
AGREED RULE
TARGET FIELD
account_ref
Preserve the stable identifier
external_account_id
billing_email
Trim and validate the format
billing_email
company_name
Match using an agreed reference
organisation_id
source_status
Translate the approved status set
lifecycle_status
Ready records continue. Ambiguous matches and rejected values keep their evidence.
Unmatched key
Hold the record
Retain the source reference and assign an owner.
Possible duplicate
Apply the matching rule
Ambiguous candidates stay in the review queue.
Conflicting values
Avoid a silent overwrite
Record which source is authoritative for the field.
Illustrative operating model · Example fields show explicit mapping and exception routes, not a customer dataset.
Illustrative brand artwork · Data cleanup and migration
Where it helps
Identify inconsistent formats, missing required fields and potential duplicate contacts, organisations or deals. Agree what can be standardised and what needs the record owner to confirm.
Map the agreed records, relationships and supported history into a new application. Test representative examples before running the full migration.
Turn a suitable spreadsheet process into a structured data model with defined fields, identifiers, relationships and ownership. Preserve necessary source references and review exceptions.
Bring approved datasets together using consistent keys, field definitions and conflict rules. Avoid silent overwrites when the sources disagree.
Prepare the required fields, categories and calculation inputs for an agreed dashboard. Make missing data and known limitations visible to the reporting owner.
Organise unmatched, rejected or ambiguous records into a review queue. Retain the source evidence and record how the issue was resolved before acceptance.
What we deliver
01
The sources, objects, fields, relationships, required history, identifiers and destination rules in scope.
02
Formatting, validation, duplicate and conflict rules, plus a clear division between automatic changes and owner decisions.
03
Sample runs, transformation checks, error reporting and reconciliation against agreed counts and representative records.
04
Timing, freeze or incremental-update arrangements, completion evidence, unresolved exceptions and rollback provisions where feasible.
Suitable when the source and destination support the required fields and relationships. We verify actual import behaviour rather than assuming a CSV preserves everything.
Useful for repeatable transfers, relationship mapping or more specific rules. Authentication, rate limits and supported history affect the plan.
A temporary controlled store can support comparison and exception review. Retention and removal are agreed so a migration does not leave unmanaged copies.
The scope names the records, fields, attachments and history to be moved. Unsupported objects, incomplete exports, retention requirements and third-party access may change what is feasible. We do not merge every apparent duplicate automatically or promise perfect data. Sensitive corrections and ownership conflicts require the appropriate client reviewer. Backups and rollback depend on the capabilities of the systems involved.
Do not promise perfect data or automatic correction of every duplicate. Define which records are in scope, how ownership is confirmed and what needs manual review.
Inspect representative data, identify issues and confirm the mapping, quality rules, access and acceptance criteria.
Run a sample migration, compare expected and actual records, resolve defects and get owner review of ambiguous cases.
Perform the agreed migration, reconcile the results and document exceptions, ownership and any follow-up work.
A /
Reconcile the trial before the cutover
Example
TRIAL RUN
Source cohort ↔ destination records
Objects
Expected entities in scope
Identifiers
Stable keys preserved
Relationships
Expected links reconciled
Representative values
Checked against the source
EVERY IN-SCOPE RECORD
Accepted
Imported and checked
Held
A named owner must review
Excluded or merged
Reason retained against the source
Differences need a disposition
Reconcile on the agreed basis for each object. Do not equate a completed import with accepted data.
Owner acceptance precedes cutover
01
Agree the update boundary
Freeze or incremental changes, timing and owners.
02
Make the acceptance decision
Review reconciliation and remaining exceptions.
03
Execute the cutover plan
Document completion and backout provisions where feasible.
Illustrative operating model · Acceptance, remaining exceptions and feasible backout provisions are agreed in scope.
Across your organisation
The first use case can sit in one department. Shared records, approvals and handoffs are designed with the wider operation in mind.
The details, answered
Yes. Data preparation can be a separate engagement or a defined phase of an integration, portal or reporting project. We focus on the fields and records the operating process actually needs.
The cutover plan depends on source and destination capabilities, data volume and the operating constraints. We agree any freeze window, incremental transfer approach and reconciliation work before the migration.
No. We can identify candidates and apply agreed reliable matching rules. Ambiguous identities, conflicting values or sensitive records go to the authorised owner for a decision.
We assess those objects explicitly. Export support, API availability, relationships, file permissions and the destination model determine which history and attachments can be preserved.
The next step
Bring a real example, your current tools and the decision or workflow you want to improve.
Plan your data migration