Open the plan with a signed-off scope statement — in-scope entities, out-of-scope exclusions, named assumptions (data freeze window, source read-access, mapping ownership) and the dependencies the migration validation workstream cannot deliver without. The impact assessment names every downstream consumer that will see the target data and the regression effort each one implies.

Score each in-scope entity for likelihood and business impact (data volume, mapping complexity, criticality, history of source-side quality issues). Trace each business requirement, mapping rule and data-contract item to the validation test(s) that will cover it. The traceability matrix becomes the "did we test what we promised" evidence at exit.

State the test objectives first, then the measurable acceptance criteria. Separate technical acceptance (counts reconcile, zero orphan rows, zero duplicate business keys, defined tolerance for known transformations) from business acceptance (sign-off from each business-domain owner against sampled records).

Phase the plan so each stage produces evidence the next depends on. The diagram below shows a typical six-phase shape: Discovery and scope lock → Design and traceability → Source baseline (profiling) → Rehearsal and reconciliation cycles → Cutover and verification → Hypercare.

Profile the source first. Record completeness, cardinality, distributions, nulls, duplicates and known data-quality issues against the mapping. The baseline becomes the frame of reference for every subsequent reconciliation and prevents the "we found bugs after cutover that were in the source all along" conversation.

At each rehearsal cycle: compare row counts at the agreed grain, then field-level reconcile matched rows against the mapping. Count matches alone are not acceptance evidence; every mismatch (and every agreed delta) is logged with a reason. A field-level diff with zero rows against the agreed tolerance is the strongest evidence this plan produces.

Row-level correctness is necessary but not sufficient. Check that parent-child relationships survived, that lookup codes still resolve, and that domain rules (balances tie out, dates sequence correctly, enumerations stay within the allowed set) still hold on the target side.

Once the target data holds, exercise it through the systems that consume it. Functional tests against the applications; integration tests against the inbound/outbound interfaces; regression tests against any downstream job that reads from the migrated store; sampled reports and operational workflows reviewed with business owners.

Scope these to the migration's actual risk profile. A target platform that will serve the same load as the source doesn't need a new load-test campaign; a replatform does. Security review covers access control on the new store and any new data pathways. Resilience and recovery exercises prove the cutover is reversible.

Document the datasets, environments, credentials and read-permissions the validation team needs. A production-like non-prod environment with a representative source snapshot is table-stakes; the plan names when it must be available, who refreshes it, and how PII is handled.

Representative query shapes the validation SDET would run. Schema and table names are fictional.

SELECT  'customers' AS entity,
        (SELECT COUNT(*) FROM src_customers
            WHERE status IN ('ACTIVE','DORMANT')) AS src_cnt,
        (SELECT COUNT(*) FROM tgt_customer)      AS tgt_cnt;

SELECT  s.customer_id,
        s.email       AS src_email, t.email       AS tgt_email,
        s.updated_at  AS src_updated, t.updated_at AS tgt_updated
FROM    src_customers s
JOIN    tgt_customer  t ON s.customer_id = t.customer_id
WHERE   COALESCE(s.email, '') <> COALESCE(t.email, '')
   OR   COALESCE(s.updated_at, '1900-01-01') <>
       COALESCE(t.updated_at, '1900-01-01');

SELECT  o.order_id, o.customer_id
FROM    tgt_order o
LEFT   JOIN tgt_customer c ON o.customer_id = c.customer_id
WHERE   c.customer_id IS NULL;

-- Sum of account balances by product line should match source control report
SELECT  product_line, SUM(current_balance) AS tgt_total
FROM    tgt_account
GROUP BY product_line
ORDER BY product_line;

SELECT  customer_id, COUNT(*) AS occurrences
FROM    tgt_customer
GROUP BY customer_id
HAVING  COUNT(*) > 1;

Classify every exception the moment it is logged: mapping defect, transform defect, source data-quality issue (pre-existing), ETL implementation bug, test data issue, approved exception. Report trend lines on each class per rehearsal cycle so leadership sees whether the migration is converging or diverging.

Rehearse the full cutover more than once in a production-like environment. Define the rollback trigger before the real cutover window begins — specific thresholds on reconciliation failures, duplicate keys, orphan rows or performance SLA breaches. Post-cutover verification runs a defined subset of the reconciliation suite against the live target.

Go/no-go is a Release Board decision, taken against the above exit criteria plus the latest risk assessment. The validation workstream brings evidence; the board brings authority.

Keep the leadership view small and consistent across cycles. One page per week: RAG for the overall migration, reconciliation trend lines (counts, field-level, exceptions), top three risks with owners and ETAs, and the decisions needed this week. Escalate anything that threatens the go-live date the moment it crosses the agreed threshold — waiting until the next status meeting erodes options.