Agent StoreBillingBilling Data Quality
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Billing Data Validation Agent

BillingBilling Data Quality

Runs pre-invoice data quality checks across customer, pricing, and usage records to catch missing fields, stale rates, or malformed data before it reaches a finalized invoice.

4
Process steps
5
Integrations
3
Data inputs

Bad data upstream, a missing tax ID, a stale price book reference, a null quantity field, an outdated billing address, routinely slips through into finalized invoices because most billing pipelines are built to process whatever data they receive rather than validate it first, and the resulting errors are usually caught only when a customer complains or an invoice bounces

Cleaning this up after the fact is far more expensive than catching it before generation, requiring void-and-reissue procedures and customer-facing corrections

This agent runs a comprehensive set of data quality checks against every account and line item scheduled for invoicing before the billing run executes, validating required fields are present, pricing references are current, quantities and amounts fall within expected ranges, and cross-referencing account status against the billing eligibility rules

Records that fail validation are held out of the run and routed to the appropriate team for correction rather than being invoiced with bad data

Before each billing run, the agent pulls the full set of accounts, subscriptions, and usage records scheduled for invoicing and runs them through a configurable rules engine checking for missing required fields, stale or invalid pricing references, quantity and amount range anomalies, and account eligibility status. Records passing all checks proceed to invoice generation; records failing any check are excluded from the run and logged to an exception queue with the specific validation failure, and a summary report is generated showing what was held and why before the run is authorized to proceed.

1

Pull Pre-Billing Dataset

  • Gather all accounts, subscriptions, and usage records scheduled for the billing run
  • Pull the current pricing and tax reference data for cross-validation
  • Confirm account status and billing eligibility flags
  • Establish the baseline record count expected for the run
Outcome: The complete dataset for the upcoming billing run is assembled for validation.
2

Run Data Quality Checks

  • Validate required fields are present and correctly formatted
  • Check pricing and tax references against current, active rate tables
  • Flag quantity or amount values outside expected historical ranges
  • Cross-check account status against billing eligibility rules
Outcome: Every record is checked against a comprehensive set of data quality rules.
3

Hold and Route Failures

  • Exclude records failing any validation check from the current run
  • Route each failure to the appropriate owning team with the specific issue noted
  • Prioritize failures by dollar impact and customer tier
  • Track failed records until corrected and cleared for the next run
Outcome: Bad data is caught and routed for correction instead of reaching a customer's invoice.
4

Report and Authorize the Run

  • Generate a summary report of records held versus passed
  • Provide billing operations a clear go/no-go signal for the run
  • Track validation failure trends over time by category
  • Feed recurring failure patterns back to upstream data owners
Outcome: Billing operations proceeds with confidence that the run is clean, with full visibility into anything held back.
Zuora
sources the pre-billing account and subscription dataset
Salesforce CPQ
provides current pricing reference data for validation
Snowflake
runs the historical quantity range comparison analysis
Jira
routes validation failures to owning teams for correction
Slack
delivers the pre-billing run go/no-go summary to billing operations