NetSuite Data Migration Strategy: Mapping, Validation & Cutover Guide

What a NetSuite Data Migration Strategy Needs to Cover

Data migration isn't a side task in a NetSuite Implementation. It decides whether day one works or falls over.

If the plan is thin, go-live stalls. Teams stop trusting numbers. Processes jam. Reporting becomes a weeks-long cleanup job.

Most teams miss this.

Moving records is the easy bit. Getting them consistent, reconciled, and provable is the real job. A common mistake we see: teams jump to templates and import tools before they've decided what should move, who owns it, and how it'll be checked.

Your plan should make those decisions explicit:

• Scope and ownership: call the shot on what master data (customers, items, vendors) and what transactional data (open orders, invoices, inventory balances) will move, and name the person who signs off.
• Source mapping: when the same record lives in multiple legacy systems, declare the system of record and how conflicts are resolved.
• Field-level rules: define transformations, defaults, normalisation, and exactly how you'll treat blanks, bad formats, and edge cases.
• Validation: reconcile record counts, totals, and ageing buckets; produce exception reports before anything is loaded.
• Cutover plan: set freeze windows, plan delta loads, map dependencies, and define clear rollback triggers.
• Post-migration QA: run end-to-end process tests and user spot checks to confirm the data behaves the way the business expects.

So what actually causes migration failures?

During audits we often see teams over-engineer mappings but under-invest in validation and cutover. The tricky part is pacing the freeze, the deltas, and the sign-offs so operations don't grind to a halt.

Build the Migration Scope Before You Move Any Data

Pause the exports.
A strong NetSuite data migration strategy lives or dies on one thing: a clear data migration scope.

We see this constantly during NetSuite cutovers. Teams start loading "what's available," and then spend weeks arguing about missing fields, mismatched totals, and reports that don't tie out. Scope isn't a technical spec. It's a business call: what must be in NetSuite on day one to run the business, what has to exist for reporting and audit, and what can live outside the ERP without hurting operations.

Start by classifying the data you have (and why it matters)

Break your scope into four buckets. Different owners. Different rules. Different risk.

1. Master data (the foundation)
   Examples: customers, vendors, items, chart of accounts.
   Why it matters: dirty master data breaks trust fast — duplicate customers, inconsistent item names — and sends transactions to the wrong accounts.
2. Open transactional data (what's still active)
   Examples: open sales orders, open purchase orders, open invoices, open credits, open AP/AR, unfulfilled shipments.
   Why it matters: this is how the lights stay on after cutover. Miss open transactions and you create manual workarounds that derail fulfilment, billing, and collections.
3. Balances (point-in-time financial truth)
   Examples: general ledger balances by account, AP/AR ageing balances, inventory on hand/value.
   Why it matters: these must reconcile to the legacy system and your financials. Bad scope here becomes unexplained variances you can't close.
4. Historical records (what happened in the past)
   Examples: closed invoices, paid bills, old purchase orders, prior-year journal entries, old inventory movements.
   Why it matters: history powers reporting, audit, customer service, and analytics — but pulling everything over adds cost, complexity, and performance risk.

Most teams accidentally over-scope history. A practical migration planning pattern: migrate master data + open transactions + required balances, and treat most historical records as an archive decision — not a default "bring it all."

Define inclusion rules dataset by dataset

Write rules a new hire could follow without asking questions. One dataset at a time: customers, vendors, items, chart of accounts, open transactions, historical records. In audits this shows up when rules are fuzzy and loaders improvise.

Examples:

• "Migrate customers with activity in the last 24 months OR with an open balance."
• "Exclude vendors marked inactive AND with no transactions in the last 36 months."
• "Migrate items that are active OR have on-hand inventory OR appear on open orders."
• "Migrate chart of accounts with mapping to NetSuite structure and reporting needs; exclude deprecated accounts with no balance and no reporting requirement."

Don't skip edge cases. Call your shots on duplicates, missing tax IDs, contacts without emails, inactive items tied to open orders, and customers with multiple accounts. The tricky part is deciding who wins when records conflict — and writing it down.

Assign data owners, not just technical owners

"Everyone owns it" means no one owns it. Name your data owners for each domain. Accountability matters.

• System owner: the person accountable for the source system extract (often IT or ERP admin).
• Business owner: the person accountable for definitions and outcomes (finance for chart of accounts and balances; operations for items; sales ops for customers; procurement for vendors).

During audits we often see gaps at the field level — credit limits, payment terms, tax codes, item categories, revenue recognition flags. Decide who makes the call. When two departments disagree, document the tie-breaker in the scope so loaders aren't stuck guessing.

Choose a migration horizon that matches how the business works

"How much history are we moving?" is one of the biggest levers in your scope. Too little and reporting and customer service suffer. Too much and you inflate the timeline and error rate.

A simple way to decide:

• Start with reporting: what finance and leadership need inside NetSuite on day one.
• Add operations: what teams need to serve customers, manage vendors, and resolve disputes.
• Push the rest to an archive (legacy read-only access, data warehouse, or exported reports) instead of NetSuite.

Most teams miss this last bit. Be explicit about what happens when someone wants an older record — where they go and what format they'll see.

Plan legacy data cleanup before mapping and loading

Legacy data cleanup sits inside scope. Not after it. We see this constantly during technical audits: "all customers" gets loaded, no duplicate logic, and now reporting is noisy on day one.

Make these decisions up front:

• Duplicates: define match logic (name + email, tax ID, address, vendor bank details) and survivorship rules.
• Naming conventions: standardise customer names, vendor names, item codes, and abbreviations so search and reporting behave.
• Required fields: decide what happens to records missing key fields (exclude, enrich, or load with placeholders plus remediation tasks).
• Status rules: define what "active/inactive" means and how it maps into NetSuite.

Why scope mistakes show up as reporting and reconciliation problems

Bad scope rarely explodes on cutover day. It leaks out over the next month.

• Financials don't match because balances were loaded without the right supporting detail, or the chart of accounts changed without clear mapping rules.
• AR/AP can't be reconciled because open transactions were only partially migrated or filtered differently by team.
• Fulfilment and billing wobble because item and customer masters aren't standardised.
• Users lose trust when they can't find the "right" customer/vendor or see three versions of the same record.

Treat migration scope like a signed contract between business and technical teams. Once you lock what moves, what gets archived, and what's excluded, build, testing, and reconciliation become measurable — and your NetSuite launch gets a lot calmer.

Map Source Data to NetSuite Fields and Rules

This is the fork in the road.
Either your NetSuite migration runs predictably, or you spend weeks chasing "why won't this import?" errors.

Map every field. Know the source. Know the NetSuite target. Know the rules that make it load cleanly, behave in approvals, and show correctly in reports. We see this constantly during NetSuite cutovers — teams skip the rule-writing, then fight avoidable rejects and bad reporting.

The job is simple to describe. Hard to execute without discipline.

What to document in your mapping sheet (and why it matters)

Don't settle for "Source column A → NetSuite field B." That's where surprises hide. Capture behaviour, not just destinations.

For each source field, include:

• Source system object and field name: table/column, export name, or CSV header. Call out the system of record when multiple systems exist.
• Field definition: plain-English meaning and allowed values. This stops "Status" or "Type" mapping to the wrong thing.
• NetSuite destination: record type (Customer, Vendor, Item, Transaction, etc.) and exact field ID. Flag custom fields and custom records.
• Required format: date patterns, number precision, boolean values, character limits, allowed list values, and any External ID rules.
• Default values: what to fill when the source is blank. Currency, subsidiary, department, location — NetSuite will demand them sometimes.
• Lookup logic: how you match values to NetSuite lists. Map "NY Warehouse" to a Location internal ID; map payment term names to Terms IDs.
• Transformation rules: explicit if/then logic for cleaning, merging, splitting, and conversions.
• Dependency notes: load order. Subsidiaries and currencies before customers; items before transactions; tax codes before taxable transaction imports.
• Validation checks: record counts, sums, spot checks, and exception reports.

Source mapping specifics for NetSuite (what teams often miss)

NetSuite is list-heavy and context-aware. The tricky part is that rules must respect list values, subsidiaries, and feature settings, or imports fail quietly.

Build mappings for:

• Subsidiaries: in OneWorld, many records must have a subsidiary. Decide: single, multiple, or derived (billing country, business unit).
• Departments and locations: if they drive reporting, approvals, or budgets, treat them as controlled lists. Define lookups and defaults, or reject records.
• Currency: if records can use multiple currencies, set currency per record, define exchange-rate handling, and normalise symbols vs ISO codes.
• Tax codes: tax is a set of conditions, not just a value. Capture how NetSuite determines tax (taxable flag, nexus, customer tax item, ship-to/bill-to). If the source uses legacy tax groups, plan a translation table.

So what actually causes imports to behave wrong? Usually missing lookups, ambiguous defaults, and load-order errors.

Data transformation rules: make them explicit and testable

"Clean the data" is not a rule. Write logic someone can implement in a script, ETL tool, or CSV pre-processor and get the same result every time.

Common, testable rules:

• Standardisation: trim whitespace, normalise casing, remove non-printable characters.
• Splitting/merging fields: split "Full Name" into First/Last; merge address lines; split a combined "City, State".
• Value translation: map source statuses to NetSuite statuses; align payment terms; convert legacy item types to NetSuite item record types.
• Date and number conversions: convert DD/MM/YYYY to the import format; fix decimal separators; treat negative credits properly.
• Derived values: if department is blank, derive from cost centre; if location is blank, derive from warehouse code.
• De-duplication logic: choose match keys (External ID, email, customer number) and define tie-breakers when duplicates appear.

Write these as testable rules. Then test them.

Common mapping challenges (and how to handle them)

Good mapping anticipates where things break.

• Inconsistent IDs across systems: when CRM and finance use different IDs, pick a primary key strategy. In NetSuite, a consistent External ID is usually safer than relying on names.
• Merged entities: if multiple legacy customers/vendors become one, document the merge rule and how you'll keep historical transactions and addresses. Store old IDs in a custom field for traceability.
• Inactive records: decide up front whether to migrate inactive customers, items, or tax codes. If you import as inactive, write it down so reporting and integrations don't choke later.
• Missing mandatory fields: required fields vary by configuration and subsidiary. Either set defaults or reject with a clear rule — don't leave it fuzzy.
• Custom record structures: complex legacy objects often need a custom record and linked children. Define parent/child keys and exact load order.

A common mistake we see: delaying decisions on merges and inactive data until cutover week.

Preserving relationships between records (where migrations break quietly)

Field mapping is half the job. Relationships make NetSuite behave like an ERP instead of a pile of flat files.

Focus on:

• Customer-to-address: give each address a unique key (External ID per address row). Set default billing/shipping flags. Match country/state rules to your NetSuite setup.
• Item-to-price level: use a stable key: item External ID + price level name + currency.
• Transaction-to-line detail: treat lines as first-class. Map item references, quantities, rates, tax codes, and per-line departments/locations.
• Lookup dependencies: any list-driven field needs deterministic lookups and a pre-load sequence. Most teams miss this and end up with partial loads.

One more thing: test with sample imports that mirror production volume and complexity.

Review mapping with both business and technical stakeholders

Get business and technical sign-off together. Business owners confirm meaning: "What does this status actually mean?" "Which department is correct for reporting?" Technical owners confirm feasibility: "Is this field writable via CSV?" "Do we need a custom field?" "What's the load order and key strategy?"

A practical rule for "done":

• Transformation rules are written in testable terms.
• Lookup tables exist for list fields (subsidiaries, departments, locations, currency, tax codes).
• A sample import proves relationships hold end to end.

Miss any of these, and your import might be technically valid but operationally wrong.

Validate Data Before and After Migration

Data validation decides whether a NetSuite migration holds on go-live or falls apart. We see this constantly during cutovers.

Treat it as two stages, every time. First, clean and prove the source is ready to move. Then, after each load, prove what landed in NetSuite is complete, accurate, and usable. The tricky part isn't "zero errors." That's a myth. The real goal: controlled errors, clear exception logs, documented fixes, and repeatable tests you can rerun in mocks and at cutover without changing the rules.

Stage 1: Pre-migration quality checks (before you load anything)

Pre-migration checks raise data quality before the first file ever hits an import queue. Skip them and you'll spend days re-running loads and chasing avoidable failures.

• Record counts (your baseline): capture counts for each object you're moving. These totals become your reconciliation anchor later.
• Mandatory fields: list the required fields for each NetSuite record type and confirm the source has values for them. "We'll fill it later" turns into partial records and broken processes.
• Duplicates: detect duplicates across customers, vendors, items, and contacts. Use rules that match reality — same tax ID beats same name.
• Format validation: standardise dates, currencies, units of measure, phone numbers, postal codes, and any strict codes.
• Referential integrity: child records must point to valid parents. If parent IDs are missing or unstable, your data will "import" and still be wrong.
• Business rules in the source: check rules like "every AR invoice has a customer and terms," "items map to a valid income account," and "each transaction belongs to a valid subsidiary."

Stage 2: Post-load verification (after each load)

Now prove what's in NetSuite matches the baseline and can run the business on day one. Counts. Totals. Usability. Most teams reconcile counts but miss balances.

A common mistake we see: teams reconcile record counts but skip balances and real workflows.

Build these checks into every migration test and repeat in user acceptance testing:

• Record counts reconciliation: compare loaded counts to your baseline and to the intended scope. Slice by subsidiary, location, currency, customer status, item type. Any variance goes in the exception log with a reason.
• Financial balances: finance should reconcile control totals and key balances — trial balance, AR/AP ageing, inventory valuation, core general ledger accounts.
• Open transactions: confirm open invoices, credits, payments, open sales orders, open purchase orders, and open vendor bills are correct and linked.
• Searches and reports: run the saved searches and standard reports the business will use on day one. This is where mapping errors light up.
• Exception logs: every run should produce logs for failed records and warnings. Track patterns. If the same root cause keeps showing up, fix it in mapping or cleansing rules — not with one-off edits.

What different teams should sign off (and what "sign off" means)

Validation ends when the right owners approve specific, evidenced results — screenshots, saved searches, exported reconciliations, and documented exceptions. Not just "looks good."

• Finance sign-off: control totals and balances reconcile as agreed. AR/AP ageing ties out. Key general ledger accounts match expectations. Tax fields behave correctly.
• Operations sign-off: items and BOMs (if used) are correct. Inventory by location makes sense. Units of measure behave. Core operational searches/reports match what teams rely on.
• Sales sign-off: customer and contact records are usable. Price levels/price lists (if in scope) work. Open opportunities/quotes/orders are correct.

Make validation documented and repeatable

Run the same scripts for every cycle. Same counts. Same reconciliation logic. Same saved searches. Same sampling plan. Most teams miss this and end up comparing apples to oranges between mock runs.

Use sample testing on purpose. Pick a meaningful slice across subsidiaries, currencies, and edge cases like credit memos, partial shipments, and multi-location items. Then document:

• what you checked,
• the results and any variances,
• the exception logs and root causes,
• the fixes applied,
• and who approved the outcome in user acceptance testing.

Repeatable evidence beats last-minute heroics. That's how validation becomes a controlled process — not a scramble before go-live.

Plan the Cutover So Go-Live Is Controlled

Finishing mappings and validations is not the finish line.
The last mile is operational. Make the migration plan a cutover plan. Predictable. Timed. Owned. Recoverable.

The aim is simple. Shorten downtime. Kill last-minute surprises during the cutover window.

Most teams miss this last mile. We see it constantly during ERP cutovers. During audits we often see teams try to improvise the runbook at 2 a.m. Don't be that team.

1) Start with a real cutover window (not a vague weekend)

Put hard timestamps on start and end. Name who's on call. Define "done." Share the schedule across finance, operations, IT, and your implementation partners.

If those groups won't commit to the same window, you're not ready to schedule go-live.

Practical moves:

• Align with business cycles: month-end close, payroll, major shipments, promotions.
• Decide what can run in parallel and what must stop.
• Map timing dependencies: bank file cutoffs, warehouse deadlines, integrations, reporting.

A common mistake we see: assuming "we'll figure it out in the window." That never ends well.

2) Run mock migrations like rehearsals, not demos

Treat mock migrations as dress rehearsals. Same people. Same tools. Same imports. Same validations. Same triage.

Capture on every mock:

• Extract, transform, load, validation — actual durations.
• Failure points: timeouts, permissions, import errors, missing dependencies.
• Workarounds that become permanent steps in the runbook.

This is not slides. This is a script. The output is your first believable migration runbook with real timestamps.

3) Write clear data freeze rules (and enforce them)

A data freeze is specific rules, not a vague "stop changing things."

Define:

• Freeze start time, and whether it covers master data, open transactions, or both.
• What's allowed (e.g. urgent customer updates) and how those are captured for the final migration.
• Who approves exceptions (real names, not "the business").
• Where changes are logged so nothing is lost.

Finance and operations must agree on what stops to protect balances, inventory, orders, and open AR/AP. IT enforces access where possible. A common mistake we see: allowing "quick fixes" with no audit trail.

4) Time the final extract and lock it down

This is where many cutovers slip. Too early, and you miss late changes. Too late, and you won't finish imports and validation before users arrive.

Set:

• Final extract timestamp(s) per dataset; masters and transactions often differ.
• A "last safe change" deadline for each team.
• A secure handoff: file naming standards, storage location, checksum/validation, and named sign-off on extract completeness.

5) Plan import sequencing around dependencies (not convenience)

Order matters. NetSuite records have dependencies, so the runbook must define sequence, owners, duration estimates, and verification after each load.

Typical sequencing pattern (tailor to your scope):

• Reference data and configuration-dependent lists
• Customers/vendors/items and related masters
• Open transactions (sales orders/purchase orders/works orders), then operational balances
• Financial open items and starting balances
• Integration enablement and scheduled jobs (only after data is stable)

Add checkpoints where finance verifies balances and operations verifies open orders/inventory before you continue. Most teams miss at least one of these gates.

6) Turn the plan into a cutover checklist with task ownership

Make the cutover checklist an operations playbook: who does what, when it happens, how success is confirmed, and what to do if it breaks.

At minimum include:

• Task owner (person and backup)
• Start time, end time, duration estimate
• Input prerequisites (files, access, approvals)
• Expected outputs (counts, logs, reconciliation report)
• Sign-off gate (named approver)
• Escalation path (who to contact, and when)

7) Build rollback in from the start (not as a "hope we don't need it")

A credible rollback plan belongs in the runbook. It spells out when you stop fixing forward and how you return to a stable state.

Document:

• Rollback triggers (time-based and quality-based)
• System of record if you revert (legacy vs NetSuite)
• How you capture transactions created during the attempt
• Who authorises rollback (names) and how the decision is announced

Rollback isn't failure. It's risk management that protects the business.

8) Make the runbook executable: timestamps, responsibilities, escalation

Your runbook must be usable by someone who wasn't in the planning meetings. Exact timestamps. File locations. Import paths. Saved searches/reports for verification. A clear escalation ladder.

Include:

• Minute-by-minute schedule for the cutover window (with buffers)
• Communication plan (Slack/Teams channel, bridge line, update cadence)
• Escalation paths (who to call for finance, operations, IT, partner support)
• A "stop/go" meeting schedule tied to sign-off checkpoints

When finance, operations, IT, and partners share the same runbook and decision gates, NetSuite go-live becomes controlled. Less downtime. Fewer surprises. Faster stabilisation after release.

Run Post-Migration QA and Stabilisation

The work doesn't stop when the last CSV finishes.
Not even close.

Most teams run into this. Those first days and weeks show how the data behaves in real processes. Small mismatches snowball into loud, costly problems. Plan a tight stabilisation window (hypercare) with named owners, clear checkpoints, and a fast loop between post-migration QA and fixes. We see the most noise in week one; a little structure here saves a lot of pain later.

Set up a structured hypercare plan (days 1–10)

Treat hypercare like a mini-project. Daily cadence. Not ad-hoc firefighting.

• Daily triage (30–45 mins): review new defects, confirm severity, assign owners, agree next actions.
• Twice-weekly checkpoints: confirm key controls are stable (order-to-cash, procure-to-pay, period close, integrations).
• Clear exit criteria: e.g. no open severity-1 issues, severity-2 trend declining, core reports reconciled, user acceptance testing sign-off on critical flows.

One issue log. Single source of truth. No side threads in chat. Each entry should include: record type, internal ID (if available), steps to reproduce, impact, severity, owner, target date, and whether the fix is NetSuite configuration, script/workflow, training, integration, or data reload.

Run post-migration QA around what users actually do

QA must mirror real work. Not just "is the record there?"

Use live-style transactions and check the downstream effects end to end. Bring users in fast. Don't wait for perfect data. Have them confirm "does this work end-to-end?" while the project team watches the flow, validates results, and fixes on the spot.

Typical flows to test:

• Order-to-cash: quotes → sales orders → fulfilments → invoices → payments → revenue posting.
• Procure-to-pay: requisitions/purchase orders → receipts → vendor bills → approvals → payment runs.
• Inventory: adjustments, transfers, picks/pack/ship, costing impacts.
• Financial close: postings, eliminations (if applicable), bank reconciliations, trial balance movement.

Confirm saved searches, reports, workflows, and controls

A common mistake we see in stabilisation: assuming "the data is present" means "operations are correct." Users live in searches, reports, and approvals. Verify that layer.

• Saved searches: confirm filters, joins, summary types, and permissions; check results against a known sample.
• Reports: reconcile key finance reports (trial balance, AR/AP ageing, inventory valuation) to legacy baselines or agreed cutover numbers.
• Workflows and approvals: confirm triggers, roles, routing, and exceptions; test edge cases (credit holds, partial receipts, approval overrides).
• Audit trail: spot-check who changed what and when, and that required fields and approvals are enforced.

Triage defects by severity and decide the fix path

Not every headache is a "migration issue." During go-lives we constantly see data blamed for problems that sit in configuration, workflow logic, or training. Speed comes from clean classification.

Suggested severity model:

• Severity 1 (stop-the-line): cannot ship, invoice, pay, or close; data corruption; access/security risk.
• Severity 2 (material workaround): process works but with manual steps; reporting materially wrong.
• Severity 3 (minor): cosmetic, training, low-impact exceptions.

Correct in NetSuite when:

• the issue is isolated (small number of records),
• it's a configuration/logic problem (workflow, tax, mapping rule),
• you can fix with controlled edits and an audit trail is acceptable.

Reload (re-import) when:

• the issue is systematic (hundreds/thousands of records),
• the same defect repeats (a mapping or transformation error),
• manual corrections would be risky or inconsistent,
• downstream processes depend on consistent values (e.g. item costing attributes, customer terms across subsidiaries).

Monitor issue patterns and stabilise with checkpoints (weeks 2–4)

As the issue log grows, slice it by record type, role, subsidiary, integration, and process step. If a theme keeps popping up — pricing, approvals, inventory statuses — hit pause on new change requests and fix the root cause. Protect the team's focus. Mixing nice-to-haves into go-live support drags stabilisation out.

Recommended checkpoints:

1. End of week 1: severity-1s closed, core transactions processing, daily reconciliations stable.
2. End of week 2: key reports reconciled, saved searches validated, workflow exceptions understood.
3. End of stabilisation: defect volume trending down, owners transitioned to business-as-usual support, documented known issues and controls.

Common NetSuite Data Migration Mistakes to Avoid

Most NetSuite data migrations don't fail on the hard stuff.
They fail on basics we see all the time during technical audits.

Biggest culprit: moving legacy data "as-is." Short-term win. Long-term mess.
You import dirty customers, items, and transactions, and day one starts with duplicates, broken links, and reports you can't trust.

During audits we often see inactive customers brought over with open balances, or items with reused codes — now you've got orphaned transactions and reconciliation headaches baked into production.

Ownership is the next gap. If nobody is clearly accountable for customers, items, and financial history, decisions get made in a panic at cutover. Then dependencies slip through the cracks — posting sequences, required reference tables, integrations — because "someone else" was supposed to own them.

Mapping trips up good teams too. Those "simple" fields hide rules: subsidiaries, statuses, tax codes, custom segments. The tricky part is the conditional stuff — one vendor per subsidiary, default status by item type, tax logic by ship-to, segment inheritance from customers to transactions. Miss those and you'll be cleaning data by hand after go-live.

Testing? Most teams undercook it. One cycle on a tiny sample won't uncover edge cases, permissions gaps, sequence issues, or timing with integrations. In audits this shows up when user acceptance testing passes on trickle data, then full-volume loads blow up with duplicates and missing dependencies.

And cutover isn't just a technical handoff. If business validation and sign-off are fuzzy, bad data gets blessed into production. Who approves customer balances, open orders, inventory valuation, and trial balance tie-outs? If that's not written down, it won't happen.

What to watch for:

• "As-is" imports creating duplicates and orphaned records
• No clear data owners for key domains (customers, items, financials)
• Hidden mapping rules not documented or tested
• Single test cycle with low volume and happy-path data
• Cutover run without business validation and formal sign-off

Questions Businesses Ask About NetSuite Data Migration

Data migration makes or breaks a NetSuite go-live. Miss a mapping or load order, and finance notices on day one.

Dependencies are the tricky bit. Customers before invoices. Items before orders. Balances only after the chart of accounts.

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