Gartner estimates that poor data quality costs organizations an average of $12.9 million per year. [¹]
Let that sit for a moment. Not $12.9 million lost to bad strategy or failed product launches. Lost to bad data.
Dirty records.
Duplicates nobody caught.
Fields that mapped to nowhere.
Now layer Salesforce data migration on top of that.
You’re not just moving rows from one system to another. You’re transferring relationships, histories, automations, and dependencies that your teams rely on every single day. One misaligned field can break a workflow. One skipped validation can cascade into months of cleanup.
And yet, most migration plans still start with the tool, not the thinking.
This guide flips that. We’ll walk through the full migration lifecycle, step by step, starting with the decisions that determine whether your data arrives intact and usable—or becomes the most expensive mess your ops team has ever inherited.
What Salesforce Data Migration Really Is
Salesforce data migration is the process of moving data, including contacts, leads, accounts, opportunities, cases, and custom objects from a source system into Salesforce.
That source might be another CRM, a legacy database, spreadsheets held together by hope and VLOOKUP, or even another Salesforce org.
Simple in theory. Complicated in practice.
Because the moment you start pulling data out of where it’s lived for years, you run into things like inconsistent formats across teams, fields that exist in one system but have no equivalent in the other, relationships between records that nobody documented, and historical data that’s technically accurate but operationally useless.
Migration isn’t a copy-paste job. It’s a translation exercise, and the quality of that translation defines whether Salesforce becomes a single source of truth or a single source of confusion.
Why Migrations Go Sideways (And It’s Rarely the Tool’s Fault)
Before getting into best practices, it’s worth understanding why Salesforce data migrations fail in the first place. The patterns are remarkably consistent.
No clear data ownership. When nobody owns data decisions, conflicts go unresolved. Teams argue over field definitions, duplicates multiply, and timelines slip while everyone assumes someone else will figure it out.
Dirty data gets migrated as-is. Migrating messy records into a clean system doesn’t fix the mess. It just relocates it. Reports lose credibility fast, and teams quietly go back to their spreadsheets.
Business context gets ignored. Data gets moved based on how it was structured in the legacy system, not how the business actually works today. Old process logic gets carried forward when it should have been retired.
Testing is an afterthought. Teams rush to go live and discover issues in production that a single sandbox test run would have caught.
The fix for all of this isn’t a better migration tool. It’s a better migration process.
The Step-by-Step Migration Process
Here’s the migration lifecycle, broken into six phases. Each one builds on the last. Skip a phase, and the next one gets exponentially harder.
Migration Flow
| Audit & Assess | → | Clean & Standardize | → | Map & Transform | → | Test in Sandbox | → | Execute & Validate |
Step 1: Audit and Assess Your Data
Before you move anything, understand what you have.
This means cataloging every data source—CRMs, databases, spreadsheets, email exports. For each source, document what fields exist, how many records you’re dealing with, who owns the data, and what the quality looks like.
Ask these questions early:
Which records are still business-relevant? Which fields map cleanly to Salesforce objects? Where are the duplicates, gaps, and inconsistencies? Are there compliance or regulatory requirements governing any of these data?
The audit phase feels slow. That’s intentional. Every hour you spend here saves you ten during execution.
Step 2: Clean and Standardize
Cleaning means removing duplicates, filling in missing values where possible, and retiring records that serve no purpose. Standardizing means making sure phone numbers, addresses, dates, and picklist values follow a single format across the board.
This is also the phase to establish naming conventions. If your legacy system has 47 variations of “United States” in the Country field, Salesforce shouldn’t inherit that problem.
A practical tip: assign a data steward for each major object (Accounts, Contacts, Opportunities). One person who owns the clean-up decisions. Without that, every ambiguous record becomes a committee discussion that goes nowhere.
Step 3: Map and Transform
This is where the real translation happens.
Mapping defines which field in the source system corresponds to which field in Salesforce. Transformation handles the format of changes—converting data types, restructuring multi-value fields, splitting or combining records to fit Salesforce’s object model.
Pay close attention to relationships. Salesforce relies heavily on parent-child hierarchies—Accounts to Contacts, Accounts to Opportunities, Opportunities to Line Items. If parent records aren’t loaded first, child records will fail on insert. Map the load order explicitly.
Document every mapping decision. Six months from now, someone will ask why a field was mapped a certain way. If the answer lives in someone’s head instead of a shared document, you have a problem.
Step 4: Choose the Right Migration Tool
The tool matters, but less than most teams think. What matters more is matching the tool to the complexity of your migration.
Migration Tool Comparison
| Tool | Best For | Volume | Complexity |
| Salesforce Data Loader | Straightforward imports and exports | Up to 5M records | Low to Medium |
| Data Import Wizard | Small, simple migrations | Up to 50K records | Low |
| MuleSoft / Informatica | Complex, multi-system migrations | Unlimited | High |
| Jitterbit / Talend | Mid-range with transformation needs | High | Medium to High |
| Salesforce Bulk API | Large-volume programmatic loads | Millions of records | Medium (dev required) |
For most B2B migrations, Salesforce Data Loader handles the job well. For enterprise-grade moves involving multiple source systems, transformations, and real-time sync requirements, platforms like MuleSoft or Informatica give you the control you need.
The mistake teams make is over-tooling simple migrations or under-tooling complex ones. Match the tool to the job.
Step 5: Test in a Sandbox (Then Test Again)
Never run a migration directly in production. Ever.
Salesforce provides sandbox environments specifically for this. Use them. Load a representative subset of your data, run your mappings, and verify the results against your source system.
What to check during testing: Do record counts match between source and target? Are parent-child relationships intact? Do picklist values, dates, and currency fields display correctly? Are automations (workflows, flows, triggers) firing as expected with the migrated data? Can users actually find and use the data in their day-to-day workflows?
Run at least two full test cycles. The first will surface mapping errors and data quality issues you missed. The second confirms your fixes actually worked.
Step 6: Execute, Validate, and Monitor
When you’re confident in your test results, schedule the production migration.
A few rules for go-live day: Migrate during off-peak hours. Batch your loads in a logical sequence—parent objects first, then children. Keep a rollback plan ready. Communicate the timeline to every team that touches Salesforce.
After the load completes, validate immediately. Run reconciliation reports comparing source system counts to Salesforce. Spot-check critical records manually. Confirm that key reports and dashboards are pulling accurate data.
Then keep monitoring. Some issues only surface when users start working with the data. Build a two-week post-migration review window where your team actively tracks and resolves data anomalies.
Salesforce Data Migration Best Practices
Beyond the steps above, here are the practices that separate clean migrations from painful ones.
Back up everything before you start. Full exports of your source data and your current Salesforce org. If something goes wrong, you need a clean restore point.
Disable automation during the load. Workflows, validation rules, and triggers can interfere with bulk data inserts. Temporarily disable them, then re-enable and test after the migration completes.
Preserve external IDs. If your source system has unique identifiers, map them to an External ID field in Salesforce. This makes it dramatically easier to match records during updates or secondary loads.
Migrate in waves, not all at once. Start with foundational objects (Users, Accounts, Contacts), then move to transactional data (Opportunities, Cases), then historical records. Each wave validates the integrity of the next.
Document everything. Every mapping decision, every data transformation rule, every exception. This documentation isn’t bureaucracy—it’s insurance.
Get users involved early. The people who use Salesforce daily will catch data issues that no automated check will find. Include them in UAT and give their feedback real weight.
The Pitfalls That Catch Even Experienced Teams
A few traps worth calling out specifically:
Migrating data you don’t need. Just because it exists in the legacy system doesn’t mean it belongs in Salesforce. Every unnecessary record adds noise and reduces trust in the system. Be ruthless about what gets migrated and what gets archived.
Ignoring data ownership post-migration. Migration is a moment. Data governance is forever. If you don’t establish who owns data quality in Salesforce after the move, you’ll be back where you started within a year.
Treating migration as purely an IT project. The technical execution matters. But the decisions about what data to keep, how to structure it, and what it means—those are business decisions. If your migration team doesn’t include business stakeholders, you’re building a system people won’t trust.
Where to Go from Here
A Salesforce data migration done well doesn’t just move records. It forces your organization to answer questions it’s been avoiding: What data do we actually rely on? What’s outdated? What are we measuring that doesn’t matter?
That’s the hidden upside of migration. It’s not just a technical project. It’s a clarity exercise.
The organizations that treat it that way—with the right planning, the right governance, and the right people in the room—don’t just end up with a working Salesforce instance. They end up with a system their teams trust.
A Quick Offer Before You Go
If you’re mid-planning or still scoping a Salesforce data migration, sometimes the most useful thing is a conversation with someone who’s seen what goes wrong—and what makes the difference.
We work with B2B teams on exactly this. Not just the technical execution, but the data governance, the sequencing decisions, and the stakeholder alignment that determines whether the migration actually sticks.
No pitch deck. Just a practical conversation about where you are and what to watch for. Write to us at info@growthnatives.com, and we’ll take it from there.
Resources
[¹] Gartner