How do I set up a data extraction platform for matching source data across ERP systems during audit work?

Here’s a practical way to set up a data extraction platform for matching source data across ERP systems during audit work.

1) Define the audit use case first

Before choosing tools, define exactly what you need to compare.

Typical audit matching scenarios:

• AP/AR transaction matching across ERP systems
• GL to subledger reconciliation
• Master data comparison: vendors, customers, materials, cost centers
• Journal entry testing across systems
• Intercompany eliminations
• Migration / carve-out validation

For each use case, define:

• Source systems involved
• Tables/files needed
• Matching keys
• Required fields
• Tolerances for differences
• Timing/frequency of extraction

2) Build the target architecture

A simple audit data platform usually has these layers:

A. Extraction layer

Pulls data from ERP systems using:

• Direct database connectors
• APIs
• Flat file exports
• ETL tools

Examples:

• SAP: extract via table export, OData, BW, RFC, or database views
• Oracle ERP: SQL views, APIs, extracts
• NetSuite/Dynamics: APIs or scheduled exports

B. Landing/staging layer

Store raw extracts exactly as received:

• CSV, JSON, XML, or parquet
• Keep source system, extract date/time, and file version
• Do not transform raw data immediately

This supports audit traceability and repeatability.

C. Standardization layer

Map source fields into a common audit schema:

• Normalize dates
• Standardize currencies
• Align chart of accounts
• Harmonize vendor/customer IDs
• Convert text and code values to canonical formats

D. Matching/reconciliation layer

Use rules to compare source records:

• Exact match
• Fuzzy match
• Key-based match
• Tolerance-based match
• One-to-many or many-to-one matching

E. Review/reporting layer

Outputs:

• Match exceptions
• Unmatched items
• Duplicate records
• Variance reports
• Audit-ready evidence logs

3) Choose the right tools

Your stack can be lightweight or enterprise-grade.

Lightweight setup

Good for smaller audit teams:

• Python or SQL for extraction and matching
• SFTP or shared storage for file movement
• PostgreSQL / SQL Server for staging
• Power BI / Tableau / Excel for review
• Git for version control

More robust setup

For larger audit or internal controls teams:

• ETL/ELT: Alteryx, Informatica, Talend, SSIS, Azure Data Factory, dbt
• Storage: SQL Server, Snowflake, Azure Synapse, Databricks, BigQuery
• Orchestration: Airflow, ADF pipelines, Control-M
• Data quality: Great Expectations, dbt tests, custom rules
• Visualization: Power BI, Tableau
• Access control and logging: Active Directory / IAM / audit logs

4) Define a common data model

This is critical for cross-ERP matching.

Create standard entities such as:

• vendor_master
• customer_master
• gl_account
• journal_entry
• invoice
• payment
• purchase_order
• goods_receipt
• cost_center

For each entity, define:

• Standard field names
• Data type
• Business meaning
• Source mappings by ERP
• Primary and alternate keys

Example:

• invoice_number
• invoice_date
• posting_date
• company_code
• vendor_id
• amount_local
• currency
• amount_base
• source_system

5) Build extraction controls

Because this is for audit, controls matter as much as data.

Include:

• Source system and table list approval
• Extract timestamp
• Record counts
• Hash totals or control totals
• File checksums
• Completeness checks
• Re-extraction versioning
• Access logs

A good control package includes:

• Start/end row counts by source
• Total amount sums by key fields
• Number of null key fields
• Duplicate key counts
• Comparison against previous extracts

6) Design the matching logic

You’ll want matching rules based on the audit objective.

Example matching methods

• Exact key match
  • Same invoice number + vendor + amount
• Composite key match
  • Company code + document number + fiscal year
• Tolerance match
  • Amount within ±1 or ±0.5% due to currency rounding
• Date window match
  • Posting date within 3 days
• Fuzzy text match
  • Supplier names or invoice references with spelling variants

Output statuses

• Matched
• Matched with variance
• Unmatched in Source A
• Unmatched in Source B
• Duplicate candidate
• Requires manual review

7) Establish data quality rules

Common audit data issues across ERPs:

• Different field lengths
• Different date formats
• Leading zeros stripped
• Currency conversion differences
• Duplicate document numbers
• Missing reference fields
• Different code lists
• One system stores posting date, another stores document date

Add validation rules for:

• Completeness
• Uniqueness
• Referential integrity
• Valid ranges
• Allowed value sets

8) Make the platform audit defensible

The platform should support evidence and repeatability.

Best practices:

• Keep raw data immutable
• Version every extract and transformation
• Document mappings and matching rules
• Store exceptions with reason codes
• Enable reruns with the same parameters
• Maintain evidence of who ran what and when

9) Set up security and access

Audit data often includes sensitive financial and personal information.

Use:

• Role-based access control
• Encryption at rest and in transit
• Least privilege access
• Separation of duties
• Masking or tokenization where needed
• Secure file transfer rather than email attachments

10) Create an operating process

A practical workflow:

1. Request source extract
2. Pull data from ERP system
3. Land raw files in staging
4. Run validation and control checks
5. Standardize fields into common schema
6. Run matching/reconciliation rules
7. Review exceptions
8. Investigate and document findings
9. Archive evidence and results

11) Start with a pilot

Don’t try to connect every ERP at once.

Pilot with:

• One entity
• One process
• Two ERP systems
• One or two tables

For example:

• AP invoices from SAP and Oracle
• Match on invoice number, vendor, amount, and date

Then expand after proving:

• extraction reliability
• mapping accuracy
• exception handling
• audit trail completeness

12) Recommended minimum setup

If you want a practical minimum viable platform:

• Extraction: SQL queries or API pulls
• Storage: SQL Server or PostgreSQL staging database
• Transformation: Python or SQL scripts
• Matching: SQL rules + Python for fuzzy matching
• Reporting: Power BI or Excel
• Control logs: audit table with counts, hashes, timestamps
• Security: role-based access + encrypted storage

Example platform flow

ERP A / ERP B / ERP C
      ↓
Raw extract landing zone
      ↓
Validation & control totals
      ↓
Standardized audit tables
      ↓
Matching engine
      ↓
Exception report / dashboard
      ↓
Audit workpapers and evidence archive

13) Key governance questions to answer

• Who approves source systems and tables?
• Who owns each data mapping?
• How are changes controlled?
• What is the re-extraction procedure?
• How are exceptions approved?
• How long are extracts retained?
• How do you prove completeness and accuracy?

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If you want, I can also provide:

1. a reference architecture diagram,
2. a sample data model for ERP matching, or
3. a step-by-step implementation plan using Python + SQL + Power BI.