Best Data Quality Checks for Integrations

A webshop order appears to be in a "processable" state in the morning, but the warehouse system shows no stock available. The invoicing program receives an incomplete shipping address, and customer service only notices the issue when the customer complains. Often, the integration is blamed, though the root cause is frequently in the data itself. The best data quality checks for integrations prevent faulty processes before they affect multiple systems and people, rather than looking for the culprit afterwards.

Integration is not merely data transfer between two applications. It conveys business decisions: what can be sold, what needs to be manufactured, where to ship, what to invoice, what stock the webshop shows, and what numbers appear in management reports. If the information transferred is uncertain, automated processes only spread the uncertainty faster.

First, clarify what constitutes good data

Many companies err by treating data quality solely as a technical issue. Is the field filled? Is the number a number? Does the product code exist? These are necessary checks, but not sufficient.

An order, for example, can be technically complete yet business-wise unprocessable. The customer selected a shipping method unavailable in the given country. The product item number exists but is being phased out. The discount amount is in the correct format but exceeds what the commercial policy or customer contract allows.

Therefore, every check should be linked to a specific business consequence. The question is not "can we validate this field?", but "what happens if we pass this data incorrectly to the next system?" Where errors cause financial discrepancies, delivery delays, production stoppages, or manual follow-ups, targeted control is justified.

The best data quality checks for integrations

Selecting the right checks depends on the process. A B2B order integration, a manufacturing data connection, and a financial system transfer carry different risks. However, some controls are fundamental in most business integrations.

Mandatory data and format checks

This is the simplest level, yet many errors slip through here. An order may require a customer ID, shipping address, payment method, tax information, and at least one order line. Product data typically needs an item number, unit, status, price, or inventory management setting.

Format checks also verify that a date is indeed a date, quantity is not text, country code is an accepted value, and the email address has a usable structure. This doesn't prevent all business problems but stops empty or incomprehensible data from quietly passing through.

However, overly strict checks can have a cost. If a newly introduced field is missing in an old partner's master data, all orders may halt. In such cases, it's not necessarily the rule that needs to be abandoned. An interim exception process, data correction task, or clearly separated old data management can be established.

Reference integrity check

In integrations, data rarely stands alone. An order line refers to a product, the order to a customer, the shipment to a warehouse, the invoice to a tax rate or cost center. If the referenced record doesn't exist, is inactive, or has a different meaning in another system, the process can easily go awry.

A common example is when the product code in the webshop doesn't match the item number used in the ERP. The discrepancy could be an extra space, a leading zero, or an old code someone still uses manually. The system then either doesn't find the product or, worse, links the line to a similar but different product.

Good checks not only verify the existence of the reference in the target system but also its usability in the given business context. Is the product sellable? Is the shipping address active? Can the cost center be used in the current period?

Range and business rule checks

A value can be formally correct yet unrealistic. Negative order quantity, zero unit price, next year's delivery date, or unusually high discount are all signs worth examining.

The rules here should follow actual operations. In a manufacturing company, for example, a work order can only be passed to production if it has an approved recipe, valid version, and proper material allocation. In a logistics process, hazardous goods may require mandatory shipping classification.

It's not advisable to automatically classify every rare value as an error. An unusual order quantity might result from a corporate framework agreement. In such cases, a warning, approval task, or exception handling is more useful than blocking the entire process.

Uniqueness and duplication recognition

Duplication often isn't obvious, yet it causes direct costs. A resent order message can create two invoices, two picking tasks, or double inventory reservation. A customer might appear twice with different spellings, leading sales and finance to see different pictures of the same partner.

Uniqueness checks require stable identifiers. Ideally, this is an external order ID, partner ID, or clear transaction key. If not available, a combination of fields like partner, date, amount, and document number must be examined. This is less certain, so human review plays a larger role.

Handling duplicate messages is especially important where network errors or timeouts prevent the sending system from knowing if the transfer succeeded. The integration must recognize: it's receiving the same transaction again, not a new business event.

Reconciliation between source and target

Successful technical data transfer doesn't prove the process is complete business-wise. Out of a thousand orders, 998 might pass, and two might end up in error status. If no one reconciles this, the two missing orders only surface later during customer complaints or monthly discrepancies.

Reconciliation checks whether the expected count, amount, status, or inventory movement is the same in both source and target. In daily order integration, this could be comparing order count and net value. In inventory transfer, it's quantities per warehouse, and in financial connections, it's document and amount matches.

This control doesn't necessarily require a complex system. The key is to have a designated responsible person, tolerance threshold, and resolution process. A rounding difference of 0.01 euros requires different handling than a missing high-value invoice.

Timeliness and sequence checks

Data can be faulty from a process perspective if it arrives too late. If webshop inventory data updates hours later, the company might sell products not in stock. If the shipment status doesn't reach customer service in time, agents unnecessarily search for answers across systems.

Sequence is needed when an event is only meaningful after a prior event. An order cancellation can't arrive before its creation. An invoice correction can't appear without the original invoice. Manufacturing statuses can't skip approved steps just because a message arrived later.

Error handling is as important as checking

A "faulty" status isn't a process by itself. If the system rejects an order, someone needs to know where the error is visible, what it means, who can fix it, and what happens after the fix. Without this, integration just creates another mailbox or spreadsheet to monitor.

Good error handling is expressed in understandable business language. "HTTP 400" might guide the technical team, but operations colleagues need to see "no active shipping address found for the order." The error message should include the affected identifier, submission time, data source, and, if possible, the next steps.

It's wise to separate fixable data issues from system errors. In the former, the business area can correct partner data or product codes. In the latter, IT must examine the connection, permissions, or service outage. Both cases can't be effectively managed on the same error reporting interface.

Start with the most costly errors

It's unnecessary to build checks for every possible field. That overloads the process and often creates more exceptions than real value. It's advisable to first gather errors that regularly cause delays, manual corrections, financial loss, or customer experience deterioration.

Review ten cases where colleagues manually corrected data between systems. What was the error? Where did it originate? Why wasn't it detected earlier? Is the data really necessary, or is a poorly designed process requesting information that's not needed? This examination often improves operations more than another technical rule.

Good integration data quality doesn't mean there are never exceptions. It means exceptions are visible, manageable, traceable, and don't force people to search across systems daily for the truth. If a check achieves this, not only does the data improve - the entire operation becomes more predictable.