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datatruth Get your Truth Score

ERP · SAP data quality for AISAP runs the plant. Its master data runs the model.

Whether you are on ECC or partway to S/4HANA, SAP holds finance, materials and supply chain, so its master data decides what AI can safely touch. Company codes, plants and Z-tables accumulated over years leave materials and business partners duplicated and unevenly kept. We compare SAP extracts with the systems beside it to show where a model would go wrong.

Get your Truth Score What to export

Starts with a 30-minute call. Commits you to nothing. Then exports only: no login, no connected app, nothing installed.

Illustrative example, not a client.

Source of truth

What SAP gets right.

Every system is built to be right about something. Knowing what SAP owns, and what it doesn’t, is where a cross-system check starts, and where the Truth Score™ (a score out of 100 for how far your data can be trusted) begins to take shape.

  • 01

    Materials and inventory

    Material master, plant data, stock and valuation.

  • 02

    The financial ledger

    Accounting documents by company code, GL account and cost centre.

  • 03

    Orders and deliveries

    Sales orders, purchase orders and goods movements.

What it isn’t the truth for

The commercial relationship. SAP knows sold-to and bill-to parties; the CRM knows the account group, the owner and the pipeline, and the two rarely share a key.

Inside the system

Common SAP data problems for AI.

What goes wrong inside SAP itself, with the objects and fields where it shows. Each one is checkable from an export.

  1. 01

    Duplicate and incomplete materials

    The same part exists under several material numbers, with clashing general (MARA) and plant (MARC) views, so stock and cost history fragment.

    MARA.MATNRMAKT.MAKTXMARC.WERKSMARA.MEINS

  2. 02

    Duplicate business partners

    Customers and vendors held more than once, or once as a customer and again as a vendor, split receivables and spend.

    KNA1.KUNNRLFA1.LIFNRBUT000.PARTNERNAME1

  3. 03

    Company codes and plants that disagree

    The same material or account classified, valued or described differently by the entity that maintains it.

    BUKRSWERKSMBEW.BKLASKNB1

  4. 04

    Z-tables and Z-fields

    Business-critical values in appended fields with no documented meaning. A model cannot interpret what only the ABAP team understands.

    ZZ* fieldsZ* tables

  5. 05

    Units and classification

    Unit-of-measure conversions and characteristic values diverge, making quantities and groupings unreliable.

    MARMMEINSclassification characteristics

Between systems

SAP looks clean on its own. The gaps are next door.

The problems that sink AI sit between SAP and the systems beside it: the same customer, person or transaction recorded differently in each. These are the gaps to test for each pairing.

ERP

SAP

Source of truth forMaterials and inventoryThe financial ledgerOrders and deliveries

SAP ↔ CRM

SalesforceDynamics 365

  • Sold-to parties with no CRM account
  • Orders for accounts the CRM marks inactive

SAP ↔ HR and payroll

WorkdayADP

  • Cost centres that don’t match the org
  • Approvers in SAP who have left

SAP ↔ Warehouse

SnowflakeBigQuery

  • Stock or margin views that differ from SAP

SAP ↔ Spreadsheets

Planningrebatesprice lists

  • Rebates agreed in sheets, absent from conditions
  • Forecasts by material codes SAP doesn’t hold
Illustrative: gaps typical between these systems, not client findings. Which ones exist in your data is what a Truth Score Read finds out.

What to export

What we need from you, in plain English.

Table extracts via SE16N, a standard report saved to spreadsheet, or an extract your basis team already produces are all fine. We specify tables and fields first. We never need a dialog user, an RFC connection or a transport.

What to exportFor whoever runs it
The material list, general and plant viewsMARA, MAKT, MARC (MATNR, MAKTX, MEINS, WERKS, MMSTA), plus MARM for units
Customers and vendorsKNA1 and LFA1, or BUT000 on S/4HANA (number, name, country, VAT field, address)
Sales orders and invoices for the period in scopeVBAK, VBAP, VBRK, VBRP (order, item, material, quantity, value, sold-to)
Accounting documents, if finance is in scopeBKPF, BSEG or ACDOCA (company code, GL account, cost centre, amount, posting date)
  • Exports only, no access to your systems
  • NDA and data processing agreement first
  • The full list of sub-processors comes with our data processing agreement, before any data moves
  • Raw exports deleted after the engagement; we keep quality metrics, not your records

AI that depends on it

AI use cases that lean on SAP.

If one of these is in a proposal or your plan, SAP is one of the systems we compare. Each page sets out what the use case needs and where it breaks.

How a Truth Score Read tests SAP

From your exports to a signed verdict.

Within 5 working days of complete exports. A fixed fee, agreed before we start, that never moves with the verdict.

  1. 01

    Export SAP, and one system beside it

    The SAP exports listed above, plus at least one neighbour, such as CRM. Under NDA, using the field list we send.

  2. 02

    Match across systems

    Our engine matches each material master and its related records across the exports by ID, name, email and domain, including the ones spelt differently in each system.

  3. 03

    Check by hand, then score

    Every red flag is checked by hand against sample records before sign-off. Then the Truth Score: each pillar shows what was measured and what wasn’t, and pillars with too little signal are marked, never guessed.

  4. 04

    Get the verdict in writing

    A signed memo: the red flags between SAP and the other systems, a Leverability read on the AI you’re considering (which AI ideas you can use now, after fixes, or not at all), a verdict (sign, fix first or walk away) and a short fix list your own team can work from.

How the Truth Score Read works

What you get back

See what comes back: a real sample memo.

One fictional B2B software company, Example Co: 72,837 records in 7 exports from its CRM, finance and HR systems, run through the same engine and the same checks as a paid Truth Score Read. Each system passes its own checks. Matched across systems, only 13% of deals tie to an invoice.

Real engine output on generated test data, not a client.

Questions

SAP and AI, answered plainly.

Anything else, ask on the 30-minute call. It commits you to nothing.

Book the call
Do you need an SAP user?

No. Table extracts or report exports produced by your team, under NDA, are enough. No dialog user, no RFC, nothing transported into your system.

We are moving to S/4HANA. Is this worth doing now?

It is a good moment. The migration will carry master data across; knowing which materials and partners are duplicated, and which Z-fields matter, shapes both the migration and any AI planned after it.

Which tables matter most for AI?

It depends on the use case. Demand and pricing lean on materials, orders and conditions; finance AI on accounting documents and cost centres. We send a specific list after the call.

Can you tell us what our Z-fields mean?

We can show which are filled, how consistently and how they relate to standard fields. Their business meaning is best confirmed with your team, and we flag every one we cannot interpret.

Find out what your SAP data can carry. Before the AI does.

Your SAP export and one other system in. The gaps between them in writing, within 5 working days of complete exports. Starts with a 30-minute call. Commits you to nothing.

Get your Truth Score See a real sample memo

Starts with a 30-minute call. Commits you to nothing.

Get your Truth Score