The short answer. GROW with SAP is SAP's fast-track route to cloud ERP for midsize companies, built on S/4HANA Cloud Public Edition with preconfigured best practices and go-live measured in weeks. Because Public Edition is greenfield only, every piece of data you want in the new system has to be migrated in deliberately, through the Migration Cockpit. That makes data readiness the practical constraint on a GROW timeline: the configuration is standardised and fast, your data is neither.

GROW is attractive precisely because it is quick. The preconfiguration removes most of the work that makes traditional implementations slow. What it does not remove is the condition of your existing data, and that is where a fast timeline meets reality.

What GROW actually is

GROW with SAP is SAP's entry point to cloud ERP, aimed at midsize companies and those new to SAP. It is built on SAP S/4HANA Cloud Public Edition, packaged with preconfigured industry best practices, the SAP Activate implementation methodology, and SAP BTP. The proposition is speed and predictability: a standardised, ready-to-run configuration rather than a bespoke build, with go-live timelines measured in weeks. It sits alongside RISE with SAP, which targets existing SAP customers and larger enterprises on Private Edition.

Public Edition is greenfield only

There is no brownfield conversion into Public Edition. Everything that arrives in the new system arrives because someone decided to migrate it.

This is the fact that shapes the whole data question. S/4HANA Cloud Public Edition does not support a brownfield system conversion from ECC. The path is a new implementation, which means the system starts empty and is populated through data migration, with the SAP Migration Cockpit as the tool. That is good news in one sense, because you are not dragging a legacy configuration forward. It also means there is no default: every master record and every open item in the new system is there as a result of an explicit decision and a successful load.

Why data becomes the constraint

In a traditional implementation the configuration work is long enough that data preparation can run alongside it without being the critical path. GROW compresses the configuration dramatically, which does not compress your data. Profiling, cleansing, de-duplicating and mapping master data takes however long your data's condition requires, and that duration is unchanged by how fast the system is configured. The result is that on a GROW project the data workstream is often the longest pole, which is the opposite of what teams expect from a programme sold on speed.

Standardisation forces scope decisions

GROW is built around a clean core and standardised processes, which has a direct data consequence: data that only made sense under a customised legacy process may have nowhere sensible to go. Rather than extending the new system to accommodate it, the usual answer is to decide whether that data is genuinely needed. This pushes scope decisions forward, which is uncomfortable but healthy. The practical implication is that a GROW data workstream should start with a deliberate decision about what migrates, what is archived and what is left behind, rather than assuming everything comes across.

Preparation is where the timeline is won

Guidance on GROW implementations consistently points at the same thing: success depends on thorough preparation, cleansing and reduction of the existing data footprint before migration. That is worth reading as a timeline statement rather than a quality one. The weeks-not-months promise holds when the data arriving at the Migration Cockpit is clean, mapped and reduced to what is needed. It does not hold when the first load reveals that master data has not been profiled, duplicates are unresolved, and mandatory fields are empty. The configuration speed is real; capturing it requires the data to be ready to match.

Start the data work before the project does

The most useful consequence of understanding that data is the critical path is what it implies about timing. Profiling and cleansing your existing master data does not require the GROW project to have started, a partner to be engaged or the target system to exist. It can begin now, against the systems you already run. For organisations considering GROW, doing that preparatory work ahead of the implementation converts the data workstream from the constraint into something largely resolved by the time configuration begins. That is the single most effective way to actually realise the speed GROW promises rather than losing it to data remediation.

What to expect from the first load

Teams new to SAP data migration are often surprised by the first load into the Migration Cockpit, because the target validates far more strictly than legacy systems typically did. Records that have worked operationally for years fail on mandatory fields that were optional before, formats that no longer conform, and references to values that do not exist in the standardised configuration. This is not a sign that something has gone wrong; it is the new system applying its standard. Expecting it, and planning to iterate through simulation until the run comes back clean, keeps the first load from reading as a crisis.

Decide what not to bring

Because Public Edition starts empty and standardised, GROW forces a question many organisations have deferred for years: how much history does the business genuinely need live in the ERP. Open items and current master data are not in question. Closed transactions going back a decade are, and the honest answer is often that they are needed for reference and audit rather than for operations, which makes archiving rather than migration the right destination. Making that decision early reduces the migration footprint, which is exactly the preparation that lets a fast timeline hold.

How deKorvai helps

GROW is one of deKorvai's documented supported programmes, alongside Greenfield and Bluefield. For a GROW project it addresses exactly the constraint described above: profiling source data to establish its condition early, cleansing and de-duplicating master data with fuzzy matching, transforming and mapping to the S/4HANA model while preserving referential integrity, and loading into the Migration Cockpit staging tables with reconciliation built in. Because the flow is rule-based and repeatable, the mock cycles a compressed timeline depends on run consistently rather than being reassembled each time.

Key takeaways

GROW is SAP's fast-track cloud ERP for midsize companies on Public Edition.
Public Edition is greenfield only, so all data arrives by deliberate migration.
Data becomes the critical path because configuration speed does not compress data work.
Preparation and footprint reduction are what let the fast timeline hold.

Frequently asked questions

What is GROW with SAP?

SAP's entry point to cloud ERP, aimed at midsize companies and those new to SAP. It is built on S/4HANA Cloud Public Edition with preconfigured industry best practices, the SAP Activate methodology and SAP BTP, with go-live timelines measured in weeks.

What is the difference between GROW and RISE with SAP?

GROW targets net-new and midsize customers on S/4HANA Cloud Public Edition with a standardised, fast deployment. RISE targets existing SAP customers and larger enterprises, and supports Private Edition with greater flexibility and control.

Can you do a brownfield conversion into GROW?

No. S/4HANA Cloud Public Edition, which GROW is built on, does not support brownfield system conversion from ECC. The path is a new implementation, so the system starts empty and is populated through deliberate data migration via the Migration Cockpit.

Why does data become the critical path on a GROW project?

Because GROW compresses configuration dramatically but does not compress data work. Profiling, cleansing, de-duplicating and mapping take however long your data's condition requires, so on a fast-track project the data workstream is often the longest pole.

SAP GROW: Data Readiness for a Fast-Track S/4HANA Move