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ERP Data Extraction for Due Diligence: Getting Clean Data From Any System

ERP data extraction is the first bottleneck in due diligence. TS teams must handle SAP, Oracle, Sage, and dozens more. Here is how to do it efficiently.

Datapack Team

ERP Data Extraction for Due Diligence: Getting Clean Data From Any System

Every due diligence engagement begins with extracting financial data from the target company's ERP system. The quality, completeness, and format of this data determines how quickly the TS team can begin analysis. Yet ERP data extraction is one of the most inconsistent steps in the deal process.

Target companies use different ERPs, different chart of accounts, different export formats, and different levels of data granularity. The TS team that can handle any ERP extract efficiently has a structural advantage over teams that lose days to data wrangling.

The ERP Landscape in Due Diligence

The ERP systems encountered in mid-market TS engagements vary by geography and company size:

Enterprise systems (SAP, Oracle, Microsoft Dynamics): Used by larger targets and subsidiaries of multinational groups. Data exports are structured but format-specific. SAP FBL3N exports differ from SAP S/4HANA downloads. Oracle exports depend on the specific module and version.

Mid-market systems (Sage, Cegid, Exact, DATEV): Common across European mid-market targets. Export capabilities vary significantly. Sage 100 exports differ from Sage X3. Cegid data may come as structured exports or PDF reports.

Small business systems (QuickBooks, Xero, FreshBooks): Frequent on smaller deals or bolt-on acquisitions. Data is generally accessible but may lack the granularity needed for detailed analysis.

Legacy and custom systems: Some targets run on proprietary or heavily customized systems with limited export capabilities. Data may only be available as printed reports or static PDFs.

Common Extraction Challenges

Format Inconsistency

Even within the same ERP, exports vary:

  • Date formats: DD/MM/YYYY vs. MM/DD/YYYY vs. YYYY-MM-DD, sometimes mixed within the same file.
  • Number formats: Comma vs. period as decimal separator. Space vs. comma as thousands separator.
  • Encoding: UTF-8, Latin-1, Windows-1252, or other encodings affect accented characters in account descriptions.
  • Delimiters: CSV files may use commas, semicolons, tabs, or pipes.

A single French Sage export with semicolon delimiters, comma decimal separators, DD/MM/YYYY dates, and Latin-1 encoding will fail to parse correctly in most standard import tools.

Missing Fields

Not all ERP exports include the fields needed for analysis:

  • GL detail may lack posting dates, only showing period numbers.
  • Account hierarchies may not be included in the export, requiring separate extraction.
  • Cost center or segment data may be in separate fields or embedded in composite account codes.
  • Currency information may be absent on single-currency exports, creating ambiguity on multi-currency deals.

Data Volume

Large targets generate substantial GL data. A company with 3 years of monthly data across 800 accounts and 500,000 journal entries produces datasets that strain Excel-based workflows. Automated data ingestion tools handle these volumes without the row-limit constraints of spreadsheet software.

Building an Extraction Workflow

Step 1: Specify Requirements Clearly

The information request should specify exactly what is needed:

  • General ledger detail with account code, account description, posting date, amount (debit/credit or signed), journal entry reference, and posting description
  • Trial balance by month for the full analysis period
  • Chart of accounts with account hierarchy
  • Preferred format: CSV or Excel with no merged cells or hidden rows

Clear specifications reduce the number of data re-requests and accelerate the data room process.

Step 2: Validate Upon Receipt

Before beginning any analytical work, validate the extracted data:

  • Row counts match expectations for the period and entity
  • Totals reconcile to the trial balance and financial statements
  • All periods are represented with no gaps
  • Account codes match the chart of accounts provided

Catching extraction issues immediately prevents downstream errors that are expensive to trace and fix.

Step 3: Normalize

Convert extracted data into a standard analytical format:

  • Parse dates into a consistent format
  • Convert number formats to standard numeric values
  • Resolve encoding issues in text fields
  • Map account codes to the standard analytical framework

Step 4: Load into Analytical Database

The normalized data feeds the analytical database that supports all workstreams: QoE, NWC, net debt, and cash flow. This database is the single source of truth for the engagement.

Automation Opportunity

ERP data extraction and normalization is the highest-ROI automation target in most TS practices. The work is repetitive, rule-based, and performed on every single deal. It also sits on the critical path: nothing else can begin until the data is clean.

Teams using purpose-built due diligence tools that handle multi-format ERP data automatically report time savings of 60 to 80 percent on the data preparation phase. This translates to 1 to 3 days saved per deal, which over a portfolio of deals significantly improves practice throughput.

The most valuable aspect is not the time saved on any single deal. It is the elimination of data quality risk. Automated parsing does not misread a semicolon-delimited CSV or transpose a date format. The analyst's time shifts from data cleanup to data analysis, where their expertise actually creates value.