Data Migration Checklist: 10 Steps to Avoid Costly Mistake
Data migration is one of those projects that sounds straightforward until you are actually doing it. Move data from System A to System B. How hard can it be?
Then you are three months in, still fixing data quality issues, explaining to stakeholders why reports do not match, and wondering how something that seemed so simple turned into such a mess.
If you have been through a data migration before, you know exactly what this looks like. If you have not, consider yourself lucky and pay attention to what follows.
The good news is that most migration disasters are predictable and avoidable. The same problems show up again and again, which means there are proven ways to sidestep them. Here are ten steps that make the difference between a migration that works and one that becomes a cautionary tale.
1. Understand What You Actually Have
This sounds obvious, but it is where most migrations start to go wrong.
Before you move anything, you need a complete picture of your current data. Not just the obvious stuff - customer records, transactions, accounts - but everything. Reference tables, historical archives, configuration settings, user permissions, custom fields someone added five years ago that half the business still relies on.
Too many migrations start with an incomplete inventory. You migrate what you know about, launch the new system, and then discover critical data was left behind. By that point, fixing it is expensive and disruptive.
We typically spend the first week of any migration project just documenting what exists. Where data lives, how much of it there is, what format it is in, how it connects to other data, who uses it, and why it matters. This feels slow at the start but prevents disasters later.
Use tools like Alteryx to profile your source data. Connect to your systems, pull samples, analyse structure and quality. Find the gaps, inconsistencies, and surprises before you start moving anything.
2. Define Success Before You Start
What does a successful migration actually look like for your business?
This is not a philosophical question. It is a practical one. Are you migrating because you need better reporting? Because the old system is being retired? Because you are consolidating multiple systems? Because compliance requirements changed?
The reason matters because it determines what you prioritise and how you measure success.
If the goal is better reporting, then data structure and completeness matter more than migrating every historical record. If the goal is regulatory compliance, then audit trails and data lineage become critical. If the goal is system consolidation, then eliminating duplication and standardising formats takes priority.
Define clear success criteria before you start. Not vague aspirations like "improve data quality," but specific, measurable outcomes like "all customer records migrated with complete contact information" or "reconciliation between old and new systems shows less than 0.1% variance."
Without clear criteria, every decision becomes a debate and every issue becomes a crisis.
3. Map Everything (And Assume Nothing)
Data mapping is tedious, unglamorous, and absolutely essential.
You need to document exactly how every field in your source system corresponds to every field in your target system. Customer name goes here. Transaction date goes there. Account balance maps to this field, but only after applying this conversion because the old system stored it differently.
Do not assume that fields with the same name mean the same thing. "Account ID" in one system might be a unique identifier. In another system, it might be a reference to a parent account. "Status" might use different codes. "Date" might use different formats or time zones.
Create detailed mapping documentation. Include transformations, business rules, validation logic, and handling for edge cases. This documentation becomes your migration blueprint and later your troubleshooting guide when something does not look right.
Alteryx excels at this because you can build the mapping as a visual workflow. Each transformation is documented, testable, and repeatable. When stakeholders ask "how does this data get converted," you can show them rather than trying to explain nested spreadsheet formulas.
4. Clean First, Migrate Second
The biggest mistake businesses make is treating migration as the opportunity to clean up data quality issues.
It is not.
If your source data is messy - duplicates, inconsistent formatting, missing values, invalid references - fix that before you migrate. Do not carry garbage into your new system hoping it will somehow be cleaner on the other side.
Migrating dirty data just gives you dirty data in a different system. Worse, it often makes problems harder to fix because now you are working in an unfamiliar environment with unfamiliar tools.
Build data quality rules into your migration workflows. Flag duplicates. Standardise formats. Validate references. Fill mandatory fields. Remove records that should not migrate.
This is where having a proper data preparation platform matters. Trying to clean millions of records manually in spreadsheets is not realistic. Alteryx can apply consistent cleaning rules across entire datasets, flag exceptions for review, and ensure only clean data makes it to the target system.
5. Test With Real Data (Not Samples)
Many migrations use small data samples for testing. A few hundred records. A subset of transactions. Enough to verify the process works.
Then they run the full migration and everything falls apart.
The problem is that edge cases only show up at scale. The customer record with special characters in the name. The transaction from the system cutover period. The account that was merged three times and has references in five different tables.
These do not appear in your carefully selected test sample. They appear when you run the full migration.
Test with realistic volumes. If you are migrating 10 million records, test with at least a million. If you have complex data relationships, make sure your test data includes those complexities. If you have historical data going back 15 years, include some of that history.
Yes, this makes testing slower and more resource-intensive. It also means you discover problems during testing rather than during go-live.
6. Build Reconciliation Into Every Step
How do you know the migration worked? Because you checked.
Not just at the end. At every step.
After extracting data from the source, reconcile record counts. After transformations, reconcile totals and validate calculations. After loading to the target, reconcile the target against the source. Build checkpoints throughout the process.
This catches problems early when they are easier to fix. If you only reconcile at the very end, you have no idea where things went wrong. Was it extraction? Transformation? Loading? You end up retracing every step trying to find the issue.
At each checkpoint, compare:
Record counts (did everything make it through?)
Financial totals (do the numbers add up?)
Key metrics (does the aggregate data match?)
Sample records (do individual records look right?)
Document every reconciliation. When stakeholders ask whether the migration was accurate, you need evidence, not just assurances.
Alteryx workflows can include automated reconciliation steps. Calculate totals from source data, compare them to target data, flag variances, and produce reconciliation reports. Everything is documented and repeatable.
7. Plan for the Unexpected
No matter how thoroughly you plan, something will go wrong.
The system you are migrating from will have undocumented features someone relied on. The target system will handle data differently than you expected. A business rule you did not know about will suddenly become critical. The migration will take longer than estimated and stakeholders will start asking questions.
Build contingency into your timeline. If you think migration will take four weeks, plan for six. If you think testing will take two weeks, allocate three. The buffer is not pessimism. It is realism.
Have rollback plans. If the migration goes badly, how do you get back to the old system? What data needs to be preserved? How quickly can you revert?
Have escalation procedures. When issues arise - and they will - who makes decisions? Who communicates with stakeholders? Who has authority to delay go-live if problems are not resolved?
Planning for failure does not cause failure. It just means you are prepared to handle it when it happens.
8. Communicate Constantly
Most migration disasters are not technical failures. They are communication failures.
Stakeholders did not know what to expect. Users were not prepared for changes. Reports looked different and nobody warned anyone. Processes changed and training was inadequate. People panicked because they did not know what was happening.
Communicate early and often. Before migration starts, explain what is happening and why. During migration, provide regular updates on progress and issues. After migration, set clear expectations about what has changed and what support is available.
Do not assume everyone understands technical details. Explain in plain language what is happening, what people will experience, and what they need to do differently.
We typically assign someone specifically to stakeholder communication during migrations. Not a technical person trying to juggle communication alongside their actual work. Someone whose job is to keep everyone informed and address concerns before they escalate.
9. Do Not Go Live on a Friday
Seriously. Just do not.
If something goes wrong - and there is a decent chance something will go wrong - you want your full team available to fix it. You want business users available to verify things look right. You want management available to make decisions.
You do not want to be frantically troubleshooting on Saturday evening while everyone is trying to enjoy their weekend.
Go live early in the week. Tuesday or Wednesday is ideal. Monday if you must, but honestly, Monday is often chaotic enough without adding a system migration.
Give yourself room to respond to issues while the business is operating normally. Save Fridays for small changes and routine updates, not major system migrations.
10. Support Does Not End at Go-Live
The migration is not finished when you flip the switch. It is finished when users are comfortable, reports are validated, processes are stable, and business operations are back to normal.
Plan for intensive support during the first few weeks after go-live. Users will have questions. Reports will look different even if they are technically correct. People will find features that work differently. There will be issues you did not catch during testing.
Have support resources ready. Dedicated help channels. Quick response times. Clear escalation paths. Patience.
Some businesses underestimate this phase and wonder why adoption is poor and complaints are high. Users are not being difficult. They are adjusting to change and they need support during that adjustment.
We typically stay closely involved for at least two weeks post-migration. Daily check-ins, rapid response to issues, proactive outreach to key users. The goal is not just successful migration but successful adoption.
The Bottom Line
Data migration is never simple, but it does not have to be disastrous.
The businesses that succeed are the ones that take time to plan properly, build quality into every step, test thoroughly, and support users through the transition. The ones that fail are the ones that rush, skip steps, assume things will work out, and hope for the best.
Jersey businesses considering migration - whether from legacy systems, between cloud platforms, or as part of system consolidation - often ask us how long it should take or what it should cost. The honest answer is: it depends on how thoroughly you want to do it.
A rushed migration might be faster and cheaper up front. But the downstream costs of data quality issues, user frustration, and operational disruption almost always exceed the money you saved by cutting corners.
And honestly, anything worth migrating is worth migrating properly.
