An SAP S/4HANA migration checklist for mid-market CIOs

A good S/4HANA migration checklist leads with the data, not the platform. Measure readiness, decide your path, protect reporting, staff senior, and commit only after you measure.

A good S/4HANA migration checklist for a mid-market CIO leads with the data, not the platform. The platform works. About 30 percent of migrations run late or over budget, and they fail on data condition and reporting, so that is where the checklist has to start. Measure readiness, decide your path on evidence, protect reporting, staff the data work senior, and commit to a date only after you have measured.

The CIO's pre-commit checklist

Clear these before you lock a date, a budget, or a partner. Each one is cheaper to settle now than to discover mid-program.

• Readiness assessment complete. A measured baseline of data condition and custom code, not a vendor's optimistic estimate.
• Data quality baseline and cleansing plan. Duplicates, gaps, and obsolete records quantified, with an owner and a sequence.
• Custom code inventory and disposition. Every object marked retire, remediate, or move to BTP. The clean-core target set.
• Path decided on evidence. Brownfield, greenfield, or RISE chosen from the assessment, not from a slide.
• Partner and budget locked early. Senior capacity booked before the 2027 crush, with a contingency for the 30% that slips.
• Reporting continuity planned. How Tableau, Power BI, and BW reporting survive the cutover, decided up front.

Source: Thinklytics SAP S/4HANA practice, 2026.

Before you commit

Have you measured the true condition of your data with evidence, not assumption? Do you have a blueprint of what moves, what is retired, and what is rebuilt? Do you know which reports break when the data moves? Who owns the data outcome, and how senior are they? Was your timeline set before or after you measured readiness? Each of these is cheap to settle now and expensive to discover mid-program.

Choosing your path

The brownfield-versus-greenfield decision is usually framed as a cost question. It is really a process question.

Brownfield vs greenfield, the honest tradeoff

• Brownfield (convert). Lower cost. Keeps configuration and history. Carries legacy technical debt and dirty data forward. Faster if the estate is clean.
• Greenfield (rebuild). Higher cost. Clean processes and clean core. Longer, pricier, and a change-management load. Right when the legacy model is broken.

The deciding question is not cost, it is how much of your current process and data is worth keeping. The assessment answers it.

Source: Thinklytics SAP data readiness practice, 2026.

Brownfield converts your existing system and is cheaper and faster, 9 to 18 months, but it carries legacy debt and dirty data forward. Greenfield rebuilds on a clean core over 18 to 36 months at higher cost and a real change-management load. Decide it from the readiness assessment, not from a slide.

During the program

Is data prep funded at 25 to 30 percent of effort? Is the data workstream senior and off the critical path? Are you cleaning data before the move, not in production, where pre-move cleansing cuts defects by about 60 percent? Is reporting continuity planned, not assumed? Is every load reconciled to source?

• 40% of the migration timeline is discovery and data cleansing. Pre-move cleansing with validation rules cuts post-migration defects by about 60% and post-go-live performance issues by about 25%. The work you skip up front returns as production incidents.

The honest test

If those questions are uncomfortable, that discomfort is cheap to resolve now and expensive at cutover. Most mid-market programs that overrun failed this checklist before they wrote a line of config. Start with the 30-day Analytics Truth Audit if you are scoping, or move into SAP data readiness and migration if you are ready to plan the move.

Frequently asked questions

What should an S/4HANA migration checklist cover first?

The data, not the platform. Measure readiness, decide your path on evidence, protect reporting, staff the data work senior, and commit to a date only after measuring.

What questions should a CIO ask before committing?

Have we measured the data with evidence? Do we have a blueprint of what moves and what is retired? Which reports break? Who owns the data outcome and how senior are they? Was the timeline set before or after we measured?

How do we choose between brownfield and greenfield?

Brownfield converts and is cheaper, 9 to 18 months, but carries legacy debt forward. Greenfield rebuilds on a clean core, 18 to 36 months and pricier. The deciding question is how much of your current process and data is worth keeping, and the assessment answers it with evidence.

What matters during the program?

Funding data prep at 25 to 30 percent of effort, keeping the data workstream senior and off the critical path, cleaning before the move, planning reporting continuity, and reconciling every load to source.

Why lead with data instead of the platform?

Because the platform works. Migrations stall on data and reporting, and about 30 percent run late or over budget for that reason. A checklist that leads with the platform misses where the risk actually is.

What is the honest test?

If the readiness questions are uncomfortable, that discomfort is cheap to resolve now and expensive at cutover. Most overrunning programs failed this checklist before they wrote a line of config.

Where do we start?

A readiness assessment if you are scoping, or a four-week Blueprint if you are ready to plan the move.