Data Quality Errors cost real money

Data Quality Error in your Favour“Bank Error in Your Favour – please collect $200”

Supply chain errors are estimated to cost the average corporation around 5% of total spend. For large organisations these errors can add up to substantial amounts of money – yet if underlying data quality root causes are not addressed they will never be adequately resolved.


Billing issues are often used by debtors to avoid making payments.[Tweet this]

Simple issues, such as a missing VAT number on an invoice, or a discrepancy between the ordered and invoiced amount frequently result in an automated hold of the payment by the debtor’s ERP.

These can take months of effort to resolve – on most cases the issue will be flagged as a late payment – not as a faulty invoice.For larger invoices the administrative costs will pale into insignificance when compared to the opportunity costs of not being paid timeously.

The reputational damage caused by faulty billing can have dire and unexpected consequences – particularly in business to consumer environments. For the City of Johannesburg, for example, billing issues and errors related to the implementation of a new billing engine caused the City to write off tens of millions. Data quality was not addressed as part of the project scope, and the City was unable to trust its own billing data. Unscrupulous rate payers may have jumped at the opportunity to declare their bills faulty, and, in some cases, valid bills may have been written off along with those in error.

Ironically, our creditors do not typically complain if they are overpaid. [Tweet this]

Spend analysis is a billion dollar industry sparked by the desire of companies to cut procurement costs.

Yet, the most common cause of overpayment  – off contract purchases due to poor vendor and product master data – may never be picked up by spend analysis tools.

For example, regional centres may enter into their own agreements with key suppliers, purchasing stock at higher than agreed rates. Or total spend with a single supplier, or on a single product, may be woefully under estimated due to multiple representations of that suppler, or product, occurring in the ERP system. The result – agreed discount thresh holds are never met as the consolidated spend is never reached for any given representation of a supplier.

Ironically, these kind of errors will never be flagged by the creditor and can run to hundreds of millions in unnecessary spend per year. Spend analysis performed against poor quality supply chain data is unlikely to identify problems as each supplier instance will be measured in isolation.

A sound foundation of quality supplier data and product data is key to successful spend analysis, accurate billing and prompt payments. A small investment in data quality can reap massive, ongoing rewards.

For more information about how quality data can cut your supply chain costs download the MoD Case Study

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