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SAP-Datenextraktion fuer Due Diligence: Nutzbare Daten aus komplexen Systemen gewinnen

SAP-Datenextraktion fuer Due Diligence erfordert die Navigation komplexer Strukturen. Erfahren Sie, wie TS-Teams SAP-Finanzdaten effizient extrahieren und normalisieren.

Datapack Team

SAP-Datenextraktion fuer Due Diligence: Nutzbare Daten aus komplexen Systemen gewinnen

SAP is the most common ERP system encountered in mid-market and large-cap Due Diligence. It is also one of the most complex to extract data from. The gap between what a TS-Team needs and what SAP readily provides creates a recurring source of delays on Mandats involving SAP-based targets.

Understanding SAP's data Architektur and extraction methods ist nicht optional for TS-Teams that work on deals involving SAP targets. It is a core competency that beeinflusst direkt Mandat timelines and Datenqualitaet.

Warum SAP-Extraktion anders ist

SAP stores Finanzdaten across multiple tables with complex relationships. Unlike simpler accounting systems where a single export provides a complete picture, SAP Datenextraktion requires understanding which tables to query, how they relate, and what filters to apply.

Multi-layer Architektur. SAP separates data across the Hauptbuch (tables BKPF/BSEG or ACDOCA in S/4HANA), sub-ledgers (Forderungen, Verbindlichkeiten, asset accounting), and reporting layers (profit center accounting, cost center accounting). A complete financial picture requires data from multiple layers.

Company code structure. SAP organizes data by company code, which may or may not correspond to legal entities. A target with three legal entities might have five company codes if historical configurations were never cleaned up. Understanding which company codes map to which entities ist essenziell for accurate consolidation.

Chart of accounts variants. SAP supports multiple Kontenplan types: the operating Kontenplan (used for daily Transaktions), the group Kontenplan (used for consolidation), and country-specific charts of accounts (used for local reporting). Das Due Diligence-Team typically needs the operating Kontenplan with its descriptions, but may also need the group chart Zuordnung for multi-entity consolidation.

Haeufige Datenanfragen fuer SAP-Zielunternehmen

TS-Teams working with SAP targets typically need the following data extracts.

Trial balance by period. The standard starting point. In SAP, this comes from the FAGLFLEXA or ACDOCA tables (S/4HANA) or the FAGLFLEXT summary table. The request should specify: company codes, fiscal years, posting periods, and whether to include special periods.

General ledger detail. Line-item detail for selected accounts, used for adjustment analysis and Transaktion testing. Sourced from BKPF (document headers) and BSEG (line items) in ECC, or ACDOCA in S/4HANA. Key fields include document number, posting date, amount, text, and reference.

Chart of accounts with descriptions. Account master data from the SKA1 and SKAT tables. This provides the account numbers, descriptions (in the relevant language), and account group classifications needed for Kontenplan Zuordnung.

Sub-ledger data. Accounts receivable aging (BSID/BSAD tables), Verbindlichkeiten aging (BSIK/BSAK tables), and fixed asset registers (ANLA/ANLB/ANLC tables) support NWC analysis and Bilanz review.

Extraktionsmethoden

There are several ways to extract data from SAP, each with trade-offs.

Standard Reports

SAP provides standard financial reports (Transaktion codes like FBL3N for GL line items, S_ALR_87012284 for Saldenliste) that koennen sein exported to Excel or CSV. Dies ist the simplest method but has limitations: report outputs may truncate long text fields, exclude certain data elements, or impose row limits.

For Due Diligence purposes, standard reports work for Saldenlistes and basic GL extracts. They are insufficient for large-volume detail data or complex multi-entity extractions.

Direct Table Extraction

Extracting data directly from SAP tables (using SE16, SQVI, or similar tools) provides the most complete and flexible data. The Analyst or IT team queries the relevant tables with appropriate filters and exports the results.

This method requires SAP access and knowledge of the data model. On many Mandats, das Zielunternehmen's IT team performs the extraction based on specifications provided by the TS-Team. The quality of the specification bestimmt direkt the quality of the extract.

Automated Extraction Tools

Purpose-built ERP Datenextraktion tools connect to SAP and extract the required data automatically. They know which tables to query, how to handle company code structures, and how to normalize the output into a format suitable for Due Diligence analysis.

Automated extraction eliminates the back-and-forth between the TS-Team and das Zielunternehmen's IT department. It also ensures consistency: every SAP extraction follows the same specification, producing the same output structure regardless of the SAP version or configuration.

Haeufige Fallstricke

SAP Datenextraktion in Due Diligence encounters several recurring issues.

Incomplete fiscal year data. SAP fiscal years may not align with calendar years. A target with a March fiscal year end stores data differently than a December year-end company. The extraction must account for the fiscal year variant.

Currency handling. SAP stores amounts in document currency, local currency, and group currency. The extraction must specify which currency the TS-Team needs. On grenzueberschreitend deals, incorrect currency selection produces data that nicht reconcile.

Special period postings. SAP allows postings to special periods (periods 13 through 16) for year-end adjustments. If these are excluded from the extraction, the Saldenliste will not tie to the audited financials.

Deleted or reversed documents. SAP retains reversed and deleted documents in its tables. Extractions must filter appropriately to avoid double-counting or including void Transaktions.

Vorbereitung der Datenanfrage

TS-Teams koennen reduzieren extraction delays by providing SAP-specific data request templates. Ein(e) gut strukturierte(r) request includes:

  1. Company codes to include (with confirmation of entity Zuordnung)
  2. Fiscal years and periods (including whether to include special periods)
  3. Currency specification (document, local, or group currency)
  4. Account ranges or account groups for GL detail extracts
  5. Output format preferences (CSV with specific delimiters, field headers, and encoding)

Teams that standardize their SAP data requests across Mandats build Effizienz into every deal involving an SAP target. This standardization is a practical application of deal workflow standardization that pays dividends on every Mandat.

Von der Extraktion zur Analyse

The extraction is only the first step. Raw SAP data requires normalization before it is ready for analysis. Account descriptions may be in das Zielunternehmen's local language. Amounts kann umfassen statistical postings. The Kontenplan structure may not align with the team's standard analytical framework.

Automated tools that handle both extraction and normalization compress the timeline from days to hours. The Analyst receives clean, mapped data ready for Saldenliste analysis statt spending the first two days of the Mandat wrestling with SAP data formats.