Integration · Data migration

Your data is a mess. And now you're about to move it into a new system.

The same customer is in there three times, spelled three ways. Half the records are out of date and nobody quite trusts the numbers. Now you are switching systems, and the fear is that the mess, and your history, either come along for the ride or vanish in the move. Here is the hard part to hear: a new system will not fix any of this. It inherits it.

What it looks like

You stopped trusting your own data a while ago.

It rarely arrives as one big failure. It is a hundred small frictions that add up until nobody believes the numbers any more.

The same customer is in the system two or three times, under slightly different names, so nobody knows which record is the real one.

Addresses, product names and codes are each written three ways, so totals never quite add up and searches miss things.

Half the records are out of date or half-filled, and you only find out when something goes wrong with a real order.

And now you are moving to a new system, and the dread is simple: that you lose years of history, or carry the whole mess across with you.

So the data that is supposed to run the business is the thing you trust least, and the move that was meant to fix it feels like the moment it could all fall apart.

Why it happens

The mess built up honestly. The move does not clear it.

None of this is anyone's fault. It is what happens to data over years of real work, and a system change does not undo it on its own.

The data was typed in by hand over years, by different people, with no shared rule for how a name, an address or a code should look.

It lives in several systems that were never kept in step, so each holds its own slightly different version of events.

A new system feels like a fresh start, but it imports whatever you give it, so the duplicates and gaps arrive on day one, and stand out more against a tidy new home.

Fields rarely line up between old and new: one system keeps a single name field where the next wants two, dates and decimals convert oddly, and figures quietly shift.

History is the first thing dropped to make a migration simpler, which is exactly the part you cannot get back.

So the question is not which system to buy. It is whether the data going into it is clean, and whether your history comes with it. The way to know is to look at the state of your data before anything moves. That is what a 30-minute review is for.

What good looks like

The mess cleaned, the history kept, the new system started right.

We look first, profiling what you have so the duplicates, gaps and inconsistencies are known before anything moves.

The data is cleaned and standardised, duplicates merged, formats made consistent, gaps filled where they can be, so one customer is one record.

Old fields are mapped carefully to new ones, and the move is done in stages and checked against the source, so you can see nothing has gone missing.

Your history comes too, with a bridge kept to anything that does not fully fit, so you keep the context, not just the opening balances.

The new system starts clean, and so does everything that reads from it: your reporting, your connected tools, and any AI you add later.

Because we are vendor-neutral, we have nothing to sell you but the work itself, cleaning your data in place if you are staying put, or cleaning it as we move you if you are switching. We do not promise that nothing can ever go wrong; we back it up, map it, move it in stages and reconcile it against the source, so you can see for yourself that it is all there. Clean data is the floor everything else stands on. You build it once, and use it everywhere.

Common questions

Messy data and migrations, answered.

No. A new system imports whatever you give it, so duplicates, gaps and inconsistent records arrive on the first day, and look worse against a clean setup. The cleaning has to happen before or during the move, not after. Treating the migration as the moment to clean is the difference between a fresh start and the same mess in a new place.
Not if the move is done properly. History is often the first thing dropped to make a migration quicker, but it is the part you cannot recover, and the part your reporting and any AI depend on. We bring it across, and where some of it does not fit the new system cleanly, we keep a bridge to it so it stays accessible rather than lost.
Yes. A lot of what we do is cleaning data in place: merging duplicates, standardising formats, filling gaps and reconciling records, with no migration involved. You do not need to switch systems to get data you can trust; you need the data sorted out, wherever it currently lives.
We back up the source first, map every field from old to new, move the data in stages rather than all at once, and reconcile the result against the original, comparing counts and checking samples, so you can see nothing is missing. We will not claim a migration can never go wrong, but it can be made safe to check rather than something to hope about.
Usually not because the move itself is hard, but because the mess gets carried over, fields do not line up between systems, formats corrupt quietly, and history gets dropped, often with no testing until it is too late. Cleaning first, mapping carefully and checking against the source removes most of the risk. The data is the hard part, not the move.

In short

  • A new system does not clean messy data; it imports whatever it is given, so duplicates, gaps and inconsistencies arrive on day one and stand out more against a clean setup.
  • Most data messes build up over years of manual entry across several systems that were never kept in step, leaving the same record stored in conflicting ways.
  • The safe way to move systems is to assess the data, clean and standardise it, map old fields to new, migrate in stages, and reconcile against the source so nothing is lost.
  • Historical data is often dropped to simplify a migration, but it is the part that cannot be recovered and the part reporting and AI tools depend on, so it should be preserved.
  • MYT Digital is vendor-neutral and cleans data either in place or as part of a migration, charging for the work rather than any software, so the data underneath reporting, integration and AI can be trusted.

Before you move a single record

Find out how clean your data really is.

Book a 30-minute review and we will look at the state of your data, where the duplicates and gaps are, and what it takes to clean it, whether you are moving systems or staying where you are.