If your migration plan starts with “export, load, done,” you are late. The cost of getting data wrong rarely shows up on the project plan, but it shows up in chargebacks, audit findings, broken forecasts, and angry customers. Gartner-cited research has long warned that a large share of migrations either fail or blow past budget and timelines, with one widely referenced figure putting it at 83%. And the pressure is only getting worse as organizations depend on data for AI and decision making, while leaders keep flagging data quality as a top operational priority.This is why I treat data migration services as a risk program first, and a data movement exercise second. If you are buying data migration services, ask how the team proves trust, not how fast it can copy tables. The hard part is proving that the target system can be trusted on day one, and that you can explain every mismatch.
Below is how I think about migration risk beyond bytes on a wire, with practical controls you can borrow.
Which underestimated data migration risks derail “go-lives”?
Most teams plan for volume and format. They do not plan for behavior.
Risks I see underestimated most often:
- Semantic drift- “Customer Status” looks the same in two systems but carries different business meaning.
- Process coupling- Upstream systems do not just feed data. They trigger workflows. When identifiers change, the workflow changes.
- Hidden retention rules- A field that “nobody uses” is often used by compliance and investigations later.
- Tooling blind spots- ETL logs tell you rows moved, not whether the business story is intact.
One data point worth keeping in view is that poor data quality has measurable cost, and leaders are actively prioritizing it. IBM’s recent writing highlights how data quality issues show up as a top priority for operations leaders. That matters because migrations are the single biggest data quality shock you can introduce in one day.
A simple way to frame legacy data risk is this: old systems can be messy, but they are at least predictable to the people who have lived with them. The moment you migrate, you lose “tribal checks” and you expose every inconsistency at once. If you do not plan for that exposure, the go-live becomes a data triage event.
Data integrity and reconciliation: the part nobody budgets enough time for
Data integrity is not a slogan. It is a set of proofs.
IBM describes data reconciliation as verifying information across systems to protect integrity and consistency. In practice, that means multi-level reconciliation:
- Record counts by entity and partition (by month, region, business unit).
- Control totals for numeric facts (sum of invoice amounts, quantity shipped, tax collected).
- Key integrity checks (no orphaned foreign keys after migration).
- Business rules checks (every active customer has a bill-to, every closed order has a close date).
- Outcome checks (critical reports and downstream feeds match on key measures).
Row counts can match while meaning is broken.
A practical reconciliation matrix
Use a matrix that forces you to name the “why” behind each check. If a check does not protect a decision, a control, or a regulated obligation, it is noise.
| What you reconcile | Why it matters | Example metric | Evidence you keep |
| Master data (customers, products) | Prevents duplicate identities and broken joins | Duplicate rate, null critical fields | Before/after profiling report |
| Transaction facts (orders, claims) | Protects revenue and operations | Control totals, exception rate | Signed control total sheet |
| Reference data (codes, statuses) | Prevents logic drift in workflows | Invalid code usage count | Mapping table with approvals |
| History and retention | Supports audits and investigations | Missing history records | Sampled audit trail extracts |
Governance during migration: who decides, who signs, who owns the mess
A migration without governance becomes a series of “temporary” decisions. Temporary decisions become permanent defects.
I prefer to keep data migration governance lightweight but clear. It is also the cleanest way to surface ugly data realities early, while there is still time to fix them. Three rules:
- Every mapping has an owner. Not a team. A name.
- Every exception has a disposition. Fix, re-map, quarantine, or accept with documented impact.
- Every acceptance has evidence. Screenshots are not evidence. Query outputs, control totals, and signed reconciliations are.
A governance model that works in real projects
Instead of a giant committee, define roles with clear authority:
- Data owner (business)- Defines meaning, approves mappings, accepts business exceptions.
- Data steward- Maintains rules, monitors quality signals, tracks recurring anomalies.
- Migration lead- Owns runbook, cutover, and issue triage flow.
- Security and compliance- Reviews retention, access, and auditability.
- Application owner- Validates downstream behavior, reports, and integrations.
| Decision area | Responsible | Accountable | Consulted | Informed |
| Field mapping and rules | Data steward | Data owner | App owner | PMO |
| Exception disposition | Migration lead | Data owner | Compliance | Support |
| Cutover timing | Migration lead | Program sponsor | App owner | Business users |
| Post-go-live defect policy | App owner | Program sponsor | Data steward | Finance, Ops |
If you want data migration services to protect the business, governance is where you spend your political capital.
Validation and controls: build a verification habit, not a final-week scramble
Validation is not a test phase. It is a habit that starts on day one.
A clean migration validation strategy has two tracks running in parallel. Treat it as a core deliverable inside data migration services, not a side activity pushed into the last sprint:
- Technical validation- schema, constraints, referential integrity, completeness.
- Business validation- report parity, process outcomes, user-critical scenarios.
The trap is thinking you can “validate at the end.” If you wait, you validate too much at once, with no time to isolate causes.
Controls that catch problems early
Controls I keep on almost every migration:
- Data profiling before mapping- quantify null rates, outliers, duplicates. This sets expectations.
- Mapping unit tests- for each conversion rule, create test cases with known inputs and expected outputs.
- Incremental reconciliation- reconcile each load wave, not only the final load.
And yes, you need sampling. But sampling is not “look at 20 random rows.” Sampling must be risk-based:
- Sample high-value customers.
- Sample edge cases and known pain points.
IBM’s view of reconciliation as a critical practice supports this idea of continuous verification, not one-time checking.
A compact control catalog you can reuse
| Control | When to run | What it prevents | Pass criteria |
| Control totals by batch | Every load | Silent truncation and partial loads | Variance within agreed threshold |
| Referential integrity checks | Every wave | Orphan records and broken joins | 0 orphans for critical entities |
| Rule-based validations | Every wave | Bad states and invalid codes | Exception rate below baseline |
| Report parity tests | Pilot and final | Executive mistrust on day one | Key KPIs match within tolerance |
| Access and retention review | Before cutover | Compliance gaps | Approved evidence and signoff |
This is where data migration services stop being a promise and start being measurable.
Defining migration success, so “go-live” is not the only scoreboard
Success is not “the new system is up.” Success is “the business can run without inventing spreadsheets to compensate.”
I define success with four lenses:
- Correctness- data matches agreed rules and tolerances.
- Completeness- required history and entities are present, with documented exclusions.
- Continuity- critical processes complete end to end with no manual workarounds.
- Explainability- for every known variance, you can explain cause, decision, and remediation plan.
Also, recognize the operational reality. A migration can amplify legacy data risk because old exceptions become visible in new reporting layers: Forrester has reported that some organizations estimate multi-million-dollar annual losses tied to poor data quality. Migrations make those losses easier to trigger.
A success checklist that fits on one page
Keep it simple: signed mappings, a reconciliation pack, a cutover runbook with rollback criteria, and a clear post-go-live defect policy.
Move data, but manage risk like its production
When I talk about data migration services, I am really talking about risk ownership. Good data migration services leave you with repeatable controls after go-live, not just a completed cutover. Movement is a milestone. Trust is the outcome. A disciplined migration validation strategy is what makes that trust stick.
If you do one thing differently on your next program, do this: treat reconciliation evidence as a deliverable, not an afterthought. Put owners on decisions. Keep data migration governance active for at least one close cycle after go-live, so fixes do not get lost. Keep the exception trail clean. You will still hit issues, because real migrations always do. The difference is you will know what is happening, why, and what you are going to do about it.
And when someone asks whether the new platform can be trusted, you will have a calm, boring answer. That is the best kind.
Sahil Farooq