Clean your purchase data before your Infor CloudSuite migration
Infor's on-premise M3 and LN customers don't all face the same deadline, but multi-tenant CloudSuite limits the customisation many on-premise deployments have relied on for years, which is usually what starts the conversation.
Where the data breaks
- Item and supplier data has been customised for the on-premise environment over many years, in ways that don't carry across cleanly.
- Multi-tenant cloud limits how much customisation is possible, so data has to fit standard structures rather than the bespoke ones it grew into.
- Items are described differently across sites, since on-premise deployments rarely enforced one format.
What it costs to leave it
Bad data doesn't stay behind when you migrate. It gets carried into the new system, where it causes extra test runs, a delayed go-live, and spend reports nobody trusts once the dust settles.
Where this fits in your migration
Plan and choose a partner
The business case is agreed and an implementation partner is chosen.
Assess the data
What exists, what to move, and what state it is in. Pearstop starts here, by checking and classifying your purchase and supplier data.
Design the new system
Processes, fields and how old data maps to new. Pearstop maps the cleaned data to the new system's fields.
Test the move
Before go-live, your partner tests the move by loading the data into a trial version of the new system, often several times. We hand over the cleaned data in the format the new system imports, so those tests don't fail on bad records.
Go live
The new system goes live for your teams.
Run
New invoices stay classified, so reporting doesn't slip back.
A mid-sized migration often takes six to twelve months; larger SAP and Oracle programmes often run a year or more. Data problems usually show up during the test runs, when there is least time to fix them.
Your teams, who own the data.
Keeps systems, access and integrations running through the move.
Sets up the new system.
Cleans and classifies the data.
The data that matters in a Infor CloudSuite migration
- The item master, which usually needs standardising before it fits CloudSuite's structures.
- Suppliers, where duplication across sites is common.
- Fitting data to standard cloud structures, rather than assuming the old customisations will carry across.
What good data looks like
- One record per supplier, with no duplicates across entities.
- Every spend line labelled to one standard (UNSPSC or your own).
- Item and material descriptions written the same way.
- Units of measure consistent.
- Inactive suppliers and items flagged, not migrated.
- Every field mapped to the new system and documented.
- Item data fitted to standard fields.
- Suppliers de-duplicated.
How Pearstop handles it
The data doesn't need to be clean first. AI classification handles the clear cases, and a person reviews anything uncertain. UNSPSC labels are mapped to Infor CloudSuite's fields, in the format your migration needs. New invoices stay classified after go-live, so reporting doesn't slip back into the same mess.
Work with your partner
Most migrations run through an implementation partner. We work alongside them, or directly with you. Your partner sets up the new system; we give them clean, classified data in the format their import tools need. Nobody's scope changes, and your partner's team can spend their time on the new system instead of fixing records.
Why start early
Bring us in when you assess the data, not after the first test run fails. You find duplicates, free-text lines and missing categories while there is still time to fix them, nothing has to be cleaned twice, and the test runs go faster because the data goes in cleanly. Some problems only show up once you look closely at how the data behaves, which is why it pays to work with someone who handles this data every day.
Frequently asked questions
What data should we clean before moving to Infor CloudSuite?
Item and supplier records, since CloudSuite's multi-tenant model has less room for the customisation that on-premise M3 or LN deployments typically accumulate, so data needs to fit standard structures before it moves.
Related reading
Send us 200 lines
We'll classify a sample of your purchase data and send it back so you can see what good data looks like before you commit to cleaning the rest.
