How We Handle Customer Data During a Platform Migration
A working note on data migration planning — what matters, what does not, and where these projects usually go sideways.
There is a short answer on data migration planning and a useful one. The short answer fits in a Slack message. The useful one depends on three things nobody writes down, so we will write them down.
Where data migration planning usually goes wrong
The complaint shows up as a symptom. A slow week, an irritated customer, a number moving the wrong way.
The cause normally sits two decisions earlier, in something that was never written down.
Patch the symptom and it returns in different clothes.
Two real shapes this takes
One common pattern we see: the product works and the process around it does not. Nothing in the code needs changing, but three people are doing manual repair work every day.
The other pattern is the reverse. Process is fine, the system cannot hold the shape the business now needs.
The fixes have almost nothing in common, so guessing is expensive.
The mistakes that repeat
A mistake teams often make with data migration planning is starting from the most complex customer. Build for them and the simple case gets buried in configuration.
- Designing for a customer you have not signed yet.
- Copying a pattern from a company with fifty engineers.
- Deferring the boring part — permissions, exports, error states — until it blocks a deal.
- Measuring activity instead of outcome.
The engineering view
From inside the codebase, data migration planning reduces to three questions. What happens when a step fails halfway. Who finds out. How you reverse it.
Design for partial failure before you need it. Step three fails after one and two already succeeded, and that is the case people skip.
Give retries a ceiling and some jitter. A retry storm is an outage you built yourself.
How we approach it step by step
- Reproduce the pain with a real case, not a description of it.
- Write the target outcome as a single number.
- Pick the smallest change that could plausibly move that number.
- Build it with a rollback path.
- Release to one team or a slice of traffic.
- Review in two weeks, then widen, revise, or delete.
Deleting is a legitimate result. It happens less often than it should.
What good practice looks like here
- One owner, named, with time actually cleared.
- Limits enforced in code so a bad day cannot become a bad invoice.
- A short written record of why the choice was made.
- Alerts that a human reads, not a dashboard nobody opens.
- A scheduled review, because every decision here has a shelf life.
Trade-offs worth saying out loud
Speed against flexibility. Managed service against control. Cheap now against cheap later. None of it is free.
This trade-off usually appears when the second customer wants something the first one didn't. That is the moment to revisit data migration planning, not before.
Common misconceptions
“We need the best available option.” You need the one your team can operate at 2am. Rarely the same thing.
“We’ll do it properly later.” Sometimes true. Put a date on later or it never arrives.
“It’s a one-off.” Anything a customer touches becomes a product, support included.
Frequently asked questions
How long does data migration planning take to get right?
A narrow first version is usually four to six weeks. Anything quoted at three months with nothing shippable in between is a risk, not a plan.
What is the most common mistake with data migration planning?
Scoping too wide. Covering every case in version one delays feedback and raises cost without a matching benefit.
How much should we budget?
Scope decides the number, but a focused first phase on work like this typically lands in the low five figures rather than a six-month programme.
When is the right time to revisit the decision?
When a second customer asks for something the first one never needed, or when volume changes by an order of magnitude.
Do we need to hire someone for this?
Not at the start. One named owner with a few protected hours a week, plus a small build team, is enough to prove value.
Wrapping up
data migration planning does not need a perfect answer. It needs a written one, an owner, and a review date.
Pick the version you can run with the team you have today, then revisit it when the constraints change.
Related reading and next steps
- AI automations — how we run this kind of work.
- MVP development — where this often connects.
- More writing from the team.
Want a second opinion on data migration planning for your setup? Book a 30-minute call. If it is not worth building, we will say so.
FAQ
Frequently asked questions
How long does data migration planning take to get right?+
A narrow first version is usually four to six weeks. Anything quoted at three months with nothing shippable in between is a risk, not a plan.
What is the most common mistake with data migration planning?+
Scoping too wide. Covering every case in version one delays feedback and raises cost without a matching benefit.
How much should we budget?+
Scope decides the number, but a focused first phase on work like this typically lands in the low five figures rather than a six-month programme.
When is the right time to revisit the decision?+
When a second customer asks for something the first one never needed, or when volume changes by an order of magnitude.
Do we need to hire someone for this?+
Not at the start. One named owner with a few protected hours a week, plus a small build team, is enough to prove value.
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