Why data integrity matters when migrating large CRM data to Salesforce.

What happens when you move mountains of data into Salesforce? A lot can go right, but there’s a hidden snag that slips by if you’re not careful: data integrity. In big migrations, the real needle-mresser isn’t whether you can push the data over. It’s whether the data you brought along still means what you meant it to mean once it lands in Salesforce. Let me walk you through why this happens, how to spot it, and what you can do to keep your data trustworthy from day one.

Data integrity: the quiet backbone of a successful move

Imagine you’re moving a library from one building to another. You’ve got thousands of cataloged books, each with a unique ID, a precise shelf location, and relationships to other items (think a series, an author’s bibliography, or cross-references). If during the move some books arrive slightly damaged, some IDs don’t match, or some relationships get shuffled, that library stops feeling reliable. The same thing happens with CRM data. When you migrate large volumes from one system to Salesforce, there are plenty of invisible changes that can erode trust: mismatched formats, missing fields, or altered relationships between records.

The core risk isn’t that Salesforce will refuse the data; it’s that the data won’t behave the way your business expects. Reports will mislead. Users will chase stale or incorrect records. Automation—like triggers, validation rules, and processes that depend on accurate IDs—will start acting up. In short, compromised data integrity creates downstream chaos, and that chaos shows up in days, not hours.

What tends to break during big migrations

Let’s break down the main culprits you’re likely to encounter, so you can start spotting them before they bite.

• Format and field mismatches: Source systems often store dates, phone numbers, or custom fields in formats that don’t map cleanly to Salesforce. A date saved as YYYYMMDD in one system may look like a string in Salesforce, or a phone field might carry extra characters or country codes. Even small mismatches can cascade into failed saves or corrupted records.

• Incomplete data: If your source has gaps—missing emails, absent account IDs, orphaned contacts—those gaps don’t magically fill themselves in Salesforce. Missing critical fields can block business processes or misrepresent a customer’s profile.

• Relationship integrity: Salesforce relies on lookups and master-detail links. If you don’t map parent-child relationships precisely, you end up with records that feel “orphaned” or out of sync. That’s a quick way to ruin revenue-related reports, activities, and ownership assignments.

• ID mapping and external keys: When you bring in external IDs, you must preserve them correctly. Misalignment here means you can’t reconcile your new data with the original system, making audits painful at best and useless at worst.

• Data quality drift during transformation: The act of transforming data—changing formats, renaming fields, or normalizing values—can introduce subtle errors. If the transformation rules aren’t well-tested, you’ll import data that looks right on the surface but doesn’t stand up to real-world use.

• Timing and delta data: During a migration window, you’ll often run incremental loads. If you don’t capture all changes or handle ongoing updates correctly, you can end up with duplicate records, conflicting versions of the same contact, or mismatched activity histories.

• Governance gaps: Without clear ownership, acceptance criteria, and rollback plans, even a technically clean migration can spin out of control when issues surface.

Post-migration validation: the moment of truth

Here’s the practical truth: post-migration validation isn’t optional. It’s how you prove that your data still behaves the way the business expects after the move. It’s the moment you confirm that what you moved and how you moved it actually aligns with reality on the ground.

A solid validation plan includes:

• Record counts and field counts: Do the numbers line up? Do field values exist where they’re supposed to? A simple reconciliation—count of records per object and the presence of key fields—can surface big gaps.

• Field-level accuracy checks: Are mandatory fields populated? Do values fall within expected ranges? Are date fields in the correct timezone and format? These checks catch format and completeness issues.

• Relationship and reference checks: Do lookups point to valid accounts, contacts, or opportunities? Are master-detail links intact? You don’t want broken references that ruin automation or reporting.

• Data quality and deduplication review: Are duplicates feeding into Salesforce? If your old system had duplicates, you may need deduplication before or after the load, depending on business rules.

• Business-rule validation: Do critical processes trigger correctly? If a contact becomes an owner or an account changes type, do workflows, approvals, and reports reflect those changes?

• Sample audits and spot checks: Random checks on a representative sample can reveal subtle drift that automated checks miss. It’s the human eye plus the automated tests working in harmony.

A practical blueprint you can adapt

Let’s map this into a workable sequence you can actually use.

1. Pre-migration groundwork

• Profile the data: which objects will you migrate, what are the field types, and what are the non-null constraints?

• Cleanse and normalize: fix obvious inconsistencies, standardize values (think country codes, industry names, or status fields), and decide on canonical forms.

• Define the map: align source fields to Salesforce fields, including any transformations (like date formats, phone normalization, or concatenating fields).

• Establish an acceptance criteria set: what constitutes “done” for each object, each field, and each relationship?

2. Move phase (with a safety net)

• Use staged loads: start with a smaller, representative sample to validate the mapping and transformations, then scale up.

• Control changes: if you’re moving live data, sign off on a cutoff time so you can reconcile delta data at the end.

• Capture robust logs: you’ll thank yourself later for detailed error logs, batch IDs, and timestamps. They’re your breadcrumbs back to the root cause.

3. Post-move validation sprint

• Run automated reconciliations: compare counts, field-level statistics, and relationship integrity between source and target.

• Execute business-process smoke tests: ensure key automations fire correctly and critical dashboards report sensibly.

• Do targeted data quality checks: focus on high-impact records—accounts with revenue, high-velocity opportunities, or customer segments that drive your service decisions.

4. Remediation and re-load

• Triage issues by impact: critical data issues get priority, while cosmetic mismatches can wait for a refinement cycle.

• Map fixes into a revised migration pass: correct source data or adjust mappings and transformations as needed.

• Validate again: re-run the validation suite until you meet the agreed acceptance criteria.

Tools and approaches that help keep data honest

You don’t have to reinvent the wheel. Several tools and platforms are commonly used to support safe, orderly migrations:

• Salesforce Data Loader and Data Loader CLI: great for bulk inserts, updates, and upserts with clear error reporting.

• ETL and integration platforms: MuleSoft, Talend, Informatica, Boomi—these give you advanced mapping, cleansing, and orchestration capabilities.

• Modern data governance: data quality tools and profiling apps help pinpoint anomalies before you ship data to Salesforce.

• Version control and sandboxing: keep your mappings and scripts under version control; run migrations first in a sandbox to catch issues who don’t want to reveal themselves in production.

A quick analogy to keep things grounded

Think of the migration as relocating a city. You’re moving houses (records), moving people (ownership and relationships), and moving the roads (links and processes). If you misplace a street, the whole commute gets longer, the buses show up in the wrong places, and people get frustrated. The post-move validation is your city’s safety inspection—checking that water lines, electricity, and road signs line up with reality. When you treat validation as a regular, non-glamorous step, you minimize outages and keep your new home humming.

Realistic expectations: what to plan for and what not to fear

• It’s normal for some issues to pop up. The goal isn’t perfection on the first pass; it’s a clear path to a clean, auditable data state and a plan to fix things quickly.

• API limits can slow you down, but they’re a known factor. Designing the migration with batches, parallelism controls, and retry logic helps you stay within those boundaries.

• The best migration teams build a feedback loop. They test early, learn fast, and adjust mappings or rules as needed. That agile mindset saves time in the long run.

A compact checklist you can take away

Before you start

• Define data owners and acceptance criteria.

• Profile data quality and map fields precisely.

• Plan a staging area and a small pilot load.

During the move

• Load in controlled batches; keep thorough logs.

• Validate mappings as you go; watch for anomalies in real time if possible.

• Communicate clearly about any data issues that arise and how you’ll fix them.

After the move

• Run exhaustive reconciliation tests and critical workflow checks.

• Review business impact with stakeholders; adjust dashboards and reports if needed.

• Document lessons learned to improve the next migration.

Closing thoughts: trust is earned, not assumed

Moving data into Salesforce is a high-stakes operation, especially when you’re handling large volumes. The hidden hero here is data integrity. With a thoughtful approach to pre-migration preparation, careful execution, and rigorous post-migration validation, you’ll protect the accuracy and usefulness of your data—and that, in turn, preserves trust across your teams, your leaders, and your customers.

If you’re exploring the topic further, you’ll find that the field rewards those who pair practical steps with a disciplined mindset. The right balance of technical rigor and human judgment makes all the difference when you’re aligning complex data across systems. And if you’re curious about concrete checklists, real-world examples, or want to compare the strengths of different migration tools, I’m glad to dig into those details with you. After all, data integrity isn’t a one-off task; it’s a continuous habit that keeps Salesforce—and every team that depends on it—moving smoothly forward.