A proxy migration manuscript is the working specification for moving digital content, records, metadata or application data between systems without losing meaning, access or accountability. It is more than a narrative plan: it should give builders, data owners, vendors and reviewers enough detail to execute the migration, verify the result and recover when assumptions fail.
For Indian organisations, the manuscript may cover a government repository, university archive, hospital system, financial platform, SaaS replacement or internal data warehouse. The systems may differ in schema, language support, identifiers, retention rules and access controls. A useful manuscript makes those differences explicit before production data is touched.
What the manuscript should achieve
A strong document answers five operational questions:
- What is moving? Identify datasets, files, records, relationships, users, permissions and historical versions.
- Why is it moving? State the business, compliance, cost, performance or preservation objective.
- How will meaning be preserved? Define field mappings, transformations, controlled vocabularies and treatment of missing values.
- How will success be proven? Set measurable checks for completeness, accuracy, usability, security and performance.
- What happens if the move fails? Document rollback, quarantine, incident escalation and communication procedures.
Avoid describing migration as a single export-and-import event. Treat it as a controlled release with discovery, rehearsal, cutover and post-migration monitoring.
Recommended structure
1. Scope, ownership and constraints
Start with an inventory. Record source systems, target systems, environments, data owners, technical owners, vendors and dependencies. Specify what is excluded; ambiguous scope is a frequent cause of late change requests.
Include:
- Record counts, file volumes, database sizes and expected growth
- Data classifications, including personal, financial, health or confidential information
- Source and target uptime windows, network constraints and storage limits
- Required languages, scripts, time zones, currencies and Indian address or identity formats
- Retention, archival, consent, audit and access-control obligations
- Named decision-makers for sign-off and incident response
If the project uses machine learning to classify or transform records, connect the plan to a broader AI document understanding guide for India. AI-assisted processing should be treated as a controlled component, not an invisible shortcut.
2. Current-state and target-state models
Document the source before designing transformations. Capture schemas, APIs, file formats, metadata profiles, identifiers, duplicate patterns, null conventions, encoding, permissions and links between entities. Include representative samples and edge cases, not only clean records.
Then define the target contract. For every source field, state its target field, data type, transformation, validation rule, default behaviour and business owner. A mapping table is usually the most useful artefact in the manuscript:
| Source | Target | Transformation | Validation | Exception handling |
|---|---|---|---|---|
| dob | date_of_birth | Convert to ISO 8601 | Valid date; no future value | Quarantine and review |
| mobile | phone_e164 | Normalise country code | Valid Indian number pattern | Preserve raw value |
| legacy_id | source_reference | Prefix with system code | Unique and immutable | Block duplicate |
For messy spreadsheets, scanned documents or inconsistent text, define a profiling and remediation stage. The practical workflow in AI-powered data cleaning for migrations in India is relevant, but automated cleaning must preserve the original value, transformation log and reviewer decision.
3. Migration method and runbook
Choose the method based on downtime tolerance, data volume and source-system behaviour:
- Big bang: one cutover; simpler operationally but higher outage and rollback risk.
- Phased migration: move business units, regions or datasets in waves; easier to learn and contain defects.
- Parallel run: operate old and new systems together for a defined period; useful for critical workflows but expensive.
- Change-data capture or incremental sync: replicate changes before final cutover; requires conflict and ordering rules.
The runbook should list prerequisites, commands or jobs, owners, start and stop criteria, checkpoints, expected duration and evidence to capture. Separate dry-run, rehearsal and production procedures. Never rely on undocumented manual fixes during cutover.
Define idempotency: rerunning a failed job should not create duplicate records or corrupt relationships. Use stable source identifiers, migration batch IDs and reconciliation reports. Encrypt data in transit and at rest, restrict production access, rotate credentials and ensure temporary extracts are deleted according to policy.
Validation that stands up to review
Validation should combine automated controls and human review. At minimum, compare source and target counts by entity, batch and status. Reconcile totals for financial or quantitative fields, verify referential integrity, inspect permissions and test search, download, export and reporting workflows.
Useful checks include:
- Hashes or checksums for files and immutable objects
- Required-field, type, range and uniqueness validation
- Duplicate and orphan detection
- Character encoding, language and date-time checks
- Sample-based visual comparison for documents and images
- Access tests using representative user roles
- Performance tests at expected Indian peak loads and network conditions
For document-heavy migrations, combine structured checks with targeted review of OCR, tables, signatures and layouts. Multimodal document understanding with DocFormer can inform experimentation, but model output should be scored against a labelled sample before it affects production records.
Set acceptance thresholds in advance. For example, zero critical security defects, 100% preservation of mandatory identifiers, and a documented disposition for every rejected record. “The data looks correct” is not an acceptance criterion.
AI-specific safeguards
AI can help classify records, detect duplicates, map fields or identify anomalies, but it introduces uncertainty. The manuscript should record the model, version, prompt or configuration, input limits, evaluation set, confidence threshold and human escalation path. Store provenance for every AI-generated decision where feasible.
Do not send sensitive Indian personal data to an external model without an approved legal, security and procurement basis. Mask or minimise data, use approved hosting, log access and test for language, script and demographic error. For high-impact decisions, require human approval and retain the source evidence. A useful risk lens is the guidance on AI for migration errors, especially for detecting silent omissions and incorrect transformations.
Cutover, rollback and governance
Define a go/no-go meeting with explicit evidence: completed rehearsal, approved reconciliation, open-defect status, backup verification, capacity confirmation and named incident leads. Freeze or record source changes during the final window. Communicate user impact, support channels and expected restoration times in clear language.
Rollback is not simply restoring a database backup. Specify the point beyond which rollback is unsafe, how new target-side changes will be handled, how users will be redirected, and who authorises recovery. Keep the source read-only where possible until acceptance is complete.
After cutover, monitor error rates, queue backlogs, missing records, access failures and user-reported discrepancies. Run a formal reconciliation after a defined period, close exceptions with evidence and archive the final manuscript, logs, mappings, approvals and lessons learned. This creates an auditable record for future migrations and grant or procurement reviews.
A practical completion checklist
Before signing off, confirm that:
- Scope, ownership and data classifications are approved.
- Source-to-target mappings and exception rules are version-controlled.
- Backups, access controls and deletion procedures are tested.
- At least one full rehearsal has completed with measured timings.
- Automated reconciliation and human sampling meet agreed thresholds.
- AI components have documented evaluation, provenance and escalation controls.
- Cutover, rollback and communications runbooks have named owners.
- Post-migration monitoring and support are funded for a defined period.
A proxy migration manuscript earns its place when another competent team can execute the plan, reproduce its checks and understand every unresolved risk. Keep it precise, versioned and evidence-led; update it after every rehearsal rather than treating it as paperwork completed at the end.