What Data Should Be Validated?
The validation scope depends on the Hedberg application and the business processes that rely on its information. Teams should prioritize fields that affect transaction processing, reporting, customer service, and financial controls.
- Customer records: Account identifiers, billing addresses, payment terms, account status, and required contact details.
- Vendor records: Supplier IDs, payment terms, remittance details, duplicate supplier entries, and required tax information.
- Product and inventory data: Item codes, units of measure, warehouse identifiers, quantities, and product status.
- Transaction records: Invoice numbers, purchase order references, dates, amounts, receipt details, and document status.
- Financial fields: General ledger accounts, cost centers, tax codes, accounting periods, and debit-credit relationships.
Validating these fields together helps preserve connections between records. A supplier invoice, for instance, should retain the correct vendor identity and purchase order reference so that accounting teams can trace the transaction through receipt, approval, and posting.
How Does Hedberg Data Validation Work?
A structured validation process establishes rules, applies checks, reviews exceptions, and records the outcome. Validation may occur during data entry, routine updates, application integrations, reporting, or system migration.
1. Define validation rules. Specify mandatory fields, accepted formats, allowed values, unique identifiers, numerical ranges, and relationships between records. A customer ID might need to exist in the customer master before a transaction can reference it.
2. Check completeness and format. Confirm that required fields contain values and that dates, amounts, codes, and identifiers follow the expected formats.
3. Verify business relationships. Confirm that transactions reference valid customers, vendors, products, purchase orders, and accounting codes. Check that related records agree with established business rules.
4. Reconcile important totals. Compare record counts, transaction amounts, inventory quantities, and financial balances with approved source reports or control totals.
5. Review exceptions. Categorize records that fail a check, identify the underlying cause, correct the source information where appropriate, and repeat the relevant validation.
6. Document results. Retain validation rules, timestamps, exception details, approvals, and reconciliation evidence so that business owners can review the results.
Practical Examples in Finance and Operations
Consider a distributor transferring 5,000 vendor records from Hedberg into a new ERP. If 4,850 records pass all required checks, the initial validation rate is calculated as follows:
Validation rate = (Records passing validation ÷ Total records tested) × 100
Worked example: (4,850 ÷ 5,000) × 100 = 97%. The remaining 150 records require review to determine whether information must be corrected, supplemented, or handled through an approved exception process. This result gives the migration team a measurable view of data readiness.
In accounts payable, invoice processing requires checks on supplier identity, invoice number, currency, tax information, amounts, and purchase order references. Validating these fields before posting helps finance teams maintain consistent accounting records and identify invoices that need additional review.
For procurement, validating requisitions, supplier records, purchase orders, and approval statuses helps maintain spend visibility and ensures that purchasing transactions follow defined authorization rules. Customer records also benefit from checks that identify missing billing information or inconsistent payment terms before those details affect order processing or receivables reporting.
Data Validation for ERP Integration and Automation
When Hedberg exchanges information with another application, validation should occur at appropriate points before records are accepted or used in downstream workflows. The integration design should specify which system owns each field, which application performs each check, and how invalid records are reported.
API Validation checks whether data submitted through an application programming interface meets defined structural, formatting, and business requirements. Master Data Validation focuses on the accuracy and consistency of foundational records such as customers, vendors, products, and accounts. Customer Master Data Validation applies these checks specifically to customer information, including identifiers, required fields, and duplicate accounts.
For finance teams, invoice automation can combine data extraction with validation, matching, general ledger coding, approval, and posting. The resource Invoice Software 2025: AI-Ready AP & Billing Guide provides additional context on invoice capture and validation approaches, while straight-through processing describes the handling of eligible invoices through a workflow without routine manual intervention.
The Hyperbots Platform supports finance and accounting automation, while integrations with leading ERPs help exchange information between connected systems. Accurate source records are essential when automating invoice processing or maintaining vendor management information. HyperLM Finance Chatbot can help finance users analyze available financial data, making consistent field definitions and trustworthy records important for meaningful reporting and insights.
Best Practices for Hedberg Data Validation
Organizations should establish a repeatable validation framework that connects technical checks with business ownership and financial controls.
- Prioritize critical fields: Start with identifiers, amounts, dates, account codes, and relationships that directly affect transactions and reporting.
- Maintain documented rules: Record validation logic, accepted values, required fields, and the business owner responsible for each rule.
- Test representative records: Include routine transactions, historical entries, inactive accounts, and approved exceptions in test scenarios.
- Track exception patterns: Categorize failures to identify recurring source-data issues and improve correction procedures.
- Reconcile independently: Compare important financial totals and record counts against trusted source reports.
- Revalidate after changes: Repeat relevant checks whenever mapping rules, source fields, or destination-system requirements change.
These practices help teams maintain reliable records throughout data migration and ongoing operations. They also establish clear evidence that the information used for financial reporting and business decisions has been checked against defined requirements.
Summary
Hedberg Data Validation checks whether business and financial information in Hedberg systems meets defined quality rules and operational requirements. It covers record completeness, field formats, business relationships, transaction accuracy, and reconciliation. By documenting validation rules, reviewing exceptions, and testing data exchanges, organizations can improve migration readiness, support dependable finance automation, and maintain accurate operational and financial reporting.