S/4HANA migration programs are well-funded, carefully scoped, and staffed with experienced system integrators. Yet a majority of them run over budget, miss deadlines, or go live with a system that underperforms from day one.
The reason is rarely technical. It’s data.
SAP Business Suite 7 core applications remain in mainstream maintenance until end of 2027, with optional extended maintenance through 2030. Of SAP’s approximately 35,000 ECC customers, roughly 14,000 had completed their migration to S/4HANA by end of 2024, according to Gartner, leaving around 21,000 still on the clock.
For procurement leaders, the decision window is now.
Every S/4HANA steering committee has an IT workstream, a system integrator, a change management plan, and a go-live date on the calendar. What most don’t have is a clear, honest answer to one foundational question:
Is your procurement data actually ready for the new system?
Here are the 5 signs your procurement data will derail your S/4HANA migration, and exactly what to do before cutover.
The problem nobody scoped
The system integrator has the technical cutover covered. Infrastructure, configuration, testing environments, and integration gateways. That’s well-understood work.
But when the migration team hits the data extraction and loading phase, they discover that procurement data, specifically supplier master, catalogs, material master, and spend taxonomy, is fundamentally unfit for the new system.
In a UKISUG survey, 66% of respondents said data management is a challenge when moving from SAP ECC 6.0 to S/4HANA. Meanwhile, 62% of organizations cite high project costs as a key migration challenge, according to the SAPinsider 2025 Executive Summary.
A Horváth study covered by CIO found that more than 60% of companies see deviations in budget, schedule, and result quality during S/4HANA migration. Unscoped data remediation is a primary driver of those overruns.
As SAP customers retire SRM and adopt Ariba or SAP Business Network for source-to-pay and supplier collaboration, SAP’s own integration documentation requires running master data export programs and mapping supplier identifiers between systems.
That’s not an optional configuration. If your supplier records are duplicated, your catalogs are unstructured, and your compliance data hasn’t been verified in years, that mapping exercise becomes the phase that stalls your entire program.
Karthik Rama, known as the “Procurement Doctor,” puts it simply on the Beyond Procurement podcast:
“You first attend to the wound, you stop the bleeding, right? Similarly, we’ll have to first look at the existing data, look at how you can cleanse it.”
The CPO didn’t create this data mess. But they’ll own the consequences when the new system underperforms. The CFO approved the migration budget. But the real cost is still surfacing.
Sign 1: Your supplier master has duplicates that nobody can reconcile
The same supplier exists under 3, 5, and sometimes 10 different records across regions. Different legal names. Different tax IDs. Different payment terms. When the migration team tries to map vendor IDs to the new system, they discover they don’t know how many suppliers they actually have.
This isn’t a reporting inconvenience.
SAP’s Ariba connectivity documentation requires mapping supplier identifiers between systems: Ariba Network IDs to SAP supplier and system IDs. You can’t map what you haven’t deduplicated.
Karthik Rama describes supplier master data as the organization’s heartbeat:
“Your heartbeat is equal to your supplier master data. And we don’t pay enough attention to it, unfortunately.”
What to do before go-live:
- Run a deduplication exercise across all supplier records.
- Standardize legal names, tax IDs, and parent-child hierarchies.
- Establish one golden record per supplier before any data gets exported to the new system.
Sign 2: Your catalogs are free-text, not structured
Product descriptions entered by humans over 15 years. “Dell Latitude” in one system, “DELL-LAT” in another, “Laptop – Dell” in a third. No common taxonomy. No attribute standards. No image specifications.
When this data moves to S/4HANA and the procurement front-end, the catalog search breaks. Guided buying fails. Spend classification becomes unreliable. Users bypass the system from day one, and maverick spend returns before the go-live celebration ends.
Megha Singh, Director of Procurement Transformation at Micron Technology, calls out the root issue on the Beyond Procurement podcast:
“Especially when you’re implementing solutions for vendor master data, it is a killer. Because Vendor Master Data is a constant change, and a lot of organizations do not have a real-time update on that.”
The same applies to catalog data. If it wasn’t governed before migration, migrating it won’t fix it.
What to do before go-live:
- Standardize product taxonomy with a locked attribute schema, consistent naming rules, and category classification that matches the target system’s data model. This is human-intensive work. Technology alone won’t get you there.
A global beverage company operating in 50+ countries faced this exact challenge.
Working with a specialist team, they standardized and enriched 8,496 SKUs across 274 catalogs, with multilingual entries, manufacturer mapping, and image standards, then loaded the clean data directly into their procurement platform.
The result: 100% visibility into indirect spend across key suppliers.
Sign 3: Your compliance certificates haven’t been verified in years
Suppliers checked “yes” to diversity certification, ISO compliance, and insurance coverage during onboarding. Nobody verified the actual certificates. Nobody tracked expiry dates. The checkbox was the beginning and end of compliance diligence.
Now the migration surfaces this because the new system expects structured compliance data with document validation. What was invisible in the old system becomes an audit gap in the new one.
If you’re migrating compliance theater instead of real compliance data, you’re building audit exposure into a system designed to eliminate it.
What to do before go-live:
- Audit your top suppliers’ compliance documentation.
- Verify certificates are current, correctly attributed, and structured in a format the new system can validate.
- Flag expired or unverifiable records before they get loaded.
Sign 4: Your spend taxonomy doesn’t translate to the new system
Categories and classification codes built for ECC don’t map cleanly to S/4HANA’s data model. Spend that was “visible” in the old system becomes invisible because the taxonomy logic doesn’t carry over. Finance loses confidence in procurement reporting. Analytics tools built on top of the new system inherit garbage classifications and produce outputs nobody trusts.
One global FMCG enterprise discovered this firsthand.
After standardizing their procurement data, they surfaced 22% of spend that had been previously uncategorized, hidden in the old system’s classification gaps.
That’s a fifth of procurement spend that finance couldn’t see, couldn’t govern, and couldn’t act on.
Megha Singh frames the broader challenge:
“Mindset, fragmented processes, and data quality are the three big killers.”
A migration doesn’t fix fragmentation. It moves it into a less forgiving environment.
What to do before go-live:
- Map your current spend taxonomy to the target system’s classification structure.
- Identify categories that don’t translate, items that fall between categories, and spend that’s currently unclassified.
- Reclassify before migration, not after.
Sign 5: Your “data migration plan” is really just an extraction plan
The system integrator has a cutover checklist. It exports data from ECC and loads it into S/4HANA. But extraction isn’t cleansing.
Loading dirty data into a new system doesn’t make it clean. It migrates every duplicate supplier, every free-text product description, every expired certificate, and every broken classification into an environment that’s less tolerant of inconsistency, not more.
Karthik Rama is blunt about what happens when teams rush this:
“Cannot just in a haste, do the merger, the system. That is a chance of adding more messy data to your existing portfolio.”
And the problem doesn’t end at go-live:
“It’s not like you did it once when you implemented the ERP or the tool, and then you’ve never done it after that. There has to be a process or procedure around it.”
What to do before go-live:
- Separate data extraction from data remediation in your project plan.
- Scope data cleansing as a parallel workstream with its own timeline, budget, and specialist resources.
- Don’t assume the SI will handle it. System integrators are excellent at technical cutover. They’re not procurement data specialists.
What the fix looks like at scale
The enterprises that get this right treat data cleansing as a specialist engagement, not an afterthought inside the SI’s workstream.
One global FMCG enterprise managing $900M in indirect spend across 50+ countries built a procurement data layer that persisted through two platform transitions: first as a punchout catalog to Ariba, then migrating the punchout from Ariba to Coupa as the business evolved.
5,000+ suppliers onboarded, 3,000+ products standardized, and a governed catalog foundation that remained intact through both transitions, because it was built independently of the platform.
ewiz procure’s managed procurement services are built for exactly this work: supplier master deduplication, catalog enrichment, compliance verification, and taxonomy standardization, delivered as a parallel workstream and loaded directly into SAP, Ariba, Coupa, or whatever system the enterprise runs.
Faster and more specialized than bolting data remediation onto an SI’s scope of work.
Where does data readiness fit in with your AI maturity?
If you’re thinking about AI-powered procurement, data readiness is Stage 1. Without clean supplier records, standardized catalogs, and reliable spend classification, AI tools will automate bad decisions faster, not make better ones.
We’ve mapped this progression in detail in our Procurement AI Maturity Curve framework, which outlines the four stages of AI readiness in procurement, from fixing the data foundation through to predictive operations.
Download the AI Maturity Curve framework
How ready is your procurement data for S/4HANA migration?
If you recognized your organization in 3 or more of the signs above, your go-live date is at risk. Not because of IT. Because of data.
Your 2027 deadline is closer than it looks.
Try our 48-hour Data Readiness Assessment.
Send us a sample export of your supplier master or catalog data today. In 48 hours, you get back:
- A clean, standardized master file
- Duplicate clusters with fuzzy matches grouped
- UNSPSC classification applied
- A fix-first priority list for your migration timeline
No pitch. No commitment. Just your data, cleaned and returned, so you can see exactly what migration-ready procurement data looks like.
