Reducing Risk in ECC to S/4HANA Migrations:

What Actually Helps

After working through a few ECC to S/4HANA migrations, you start to see the same pattern repeat.

The data usually moves.The tools usually work.

But sooner or later, someone asks the question that slows everything down:

“How do we know this is right?”

That question doesn’t come from IT. It usually comes from Finance, Audit, or leadership — and it’s rarely answered well by load logs alone.

Over time, one approach has proven consistently useful:

Adding a small, independent validation and reconciliation layer outside of SAP, using Python.

Not to replace SAP migration tools — but to support them.

Why this gap exists

SAP provides capable migration options:

• Migration Cockpit

• SLT

• BODS

• Custom ABAP extractors

They’re good at moving data from source to target.

What they don’t always provide is a clear way to answer:

• Are records missing?

• Why did totals change between mock loads?

• What actually changed since the last run?

• Can we show evidence without pulling multiple SAP reports?

Those questions usually show up late, when timelines are already tight.

Why Finance (FI) should be the anchor

If there’s one area that determines whether a migration is trusted, it’s Finance.

FI data:

• Drives statutory reporting

• Has executive visibility

• Must reconcile exactly

• Leaves little room for interpretation

If Finance isn’t comfortable, go-live conversations become difficult very quickly.

That’s why this validation approach almost always starts with FI.

What this looks like in practice (FI example)

Instead of relying only on SAP logs, teams extract source and target data (however the project already does it) and run simple, repeatable checks outside SAP.

Example: basic FI reconciliation by company code and year

import pandas as pd

def reconcile_fi_totals(source_df, target_df,
                        keys=("BUKRS", "GJAHR"),
                        amount_col="HSL"):
    """
    Reconciles FI totals between source and target extracts.
    Designed for mock-load comparison and sign-off evidence.
    """

    for df in (source_df, target_df):
        df[amount_col] = pd.to_numeric(df[amount_col], errors="coerce").fillna(0)

    src = (
        source_df
        .groupby(list(keys), dropna=False)[amount_col]
        .sum()
        .reset_index(name="source_total")
    )

    tgt = (
        target_df
        .groupby(list(keys), dropna=False)[amount_col]
        .sum()
        .reset_index(name="target_total")
    )

    recon = src.merge(tgt, on=list(keys), how="outer").fillna(0)
    recon["variance"] = recon["target_total"] - recon["source_total"]

    return recon.sort_values("variance", key=abs, ascending=False)

# Example usage:
# source = pd.read_csv("FI_source_extract.csv", dtype=str)
# target = pd.read_csv("FI_target_extract.csv", dtype=str)
# report = reconcile_fi_totals(source, target)
# report.to_csv("FI_reconciliation.csv", index=False)

This kind of output answers Finance questions quickly and clearly — without a long explanation.

On more than one project, having this report avoided a late reload simply because everyone could see where differences came from.

Tracking what changed between mock loads

One of the most common arguments during migration testing is “Nothing changed” — even when something clearly did.

A simple hash-based comparison removes that ambiguity.

import pandas as pd
import hashlib

def compute_delta(old_df, new_df, key):
    def hash_row(row):
        return hashlib.sha256(
            "|".join("" if pd.isna(v) else str(v) for v in row.values)
            .encode("utf-8")
        ).hexdigest()

    old = old_df.copy()
    new = new_df.copy()

    old["_hash"] = old.drop(columns=[key]).apply(hash_row, axis=1)
    new["_hash"] = new.drop(columns=[key]).apply(hash_row, axis=1)

    merged = new.merge(old[[key, "_hash"]], on=key, how="left",
                       suffixes=("_new", "_old"))

    inserts = merged[merged["_hash_old"].isna()][key]
    updates = merged[
        (~merged["_hash_old"].isna()) &
        (merged["_hash_new"] != merged["_hash_old"])
    ][key]

    deletes = old[~old[key].isin(new[key])][key]

    return {
        "inserts": inserts.tolist(),
        "updates": updates.tolist(),
        "deletes": deletes.tolist()
    }

# Example:
# delta = compute_delta(mock1_df, mock2_df, key="BELNR")

This makes discussions factual instead of speculative.

How the same idea applies to MM and SD

MM (Materials Management)

MM migrations usually involve volume:

• Vendors

• Materials

• Purchasing documents

• Inventory balances

Problems are often small but numerous.

A simple consistency check catches common issues early:

def find_orphan_items(headers_df, items_df, header_key="EBELN"):
    return items_df[~items_df[header_key].isin(headers_df[header_key])]

# Example:
# ekko = pd.read_csv("EKKO.csv", dtype=str)
# ekpo = pd.read_csv("EKPO.csv", dtype=str)
# orphans = find_orphan_items(ekko, ekpo)

This kind of check is far more effective than manual sampling when volumes are large.

SD (Sales & Distribution)

SD issues often appear late:

• Missing partner roles

• Incomplete sales area data

• Differences between mock runs

Delta comparisons between runs help teams focus only on what actually changed instead of revalidating everything during UAT.

Archiving and ArchiveLink migrations: the quiet risk

Archiving migrations are often treated as lower risk because they don’t block transactions.

In reality, they introduce compliance and audit exposure.

One simple but effective control is file integrity verification.

from pathlib import Path
import hashlib

def sha256(path: Path):
    h = hashlib.sha256()
    with path.open("rb") as f:
        for chunk in iter(lambda: f.read(1024 * 1024), b""):
            h.update(chunk)
    return h.hexdigest()

# Example:
# assert sha256(Path("before.pdf")) == sha256(Path("after.pdf"))

This provides clear proof that documents were not altered during migration — something screenshots can’t do.

Why this approach works

This pattern works because it is:

• Non-invasive

• Tool-independent

• Repeatable across mock loads

• Easy to explain to non-technical stakeholders

Most importantly, it provides an independent view of the data. That independence changes the tone of discussions very quickly.

Final thought

ECC to S/4HANA migrations don’t usually fail because data can’t be moved.

They struggle when teams can’t clearly show that the data is complete, consistent, and understood.

Adding a lightweight validation and reconciliation layer outside SAP won’t change your migration tools — but it often changes the outcome.

And if it prevents even one late escalation, it’s worth the effort.