True Data Quality Requires The Application of Standards

Terms such as "High" or "Poor" quality are meaningless on their own. In order to put a value on data quality, we have to be able to measure it.

Flawed product information affects both ends of every individual trading relationship, whether supplier-to-wholesaler, supplier-to-retailer, or retailer-to-consumer. In fact data quality is a relative term because customers each have their own specific needs, plus common industry practices. Trading partners therefore need to agree a scale by which quality is measured.

Common industry or global product data quality standards are the best choices for agreement between trading partners, because they provide flexibility whilst incorporating industry know-how and best practice. Data quality standards also come with the mechanisms to be extended and improved as business evolves.

Data scorecarding is the means by which product information can be measured, where the terms of reference are defined by standards. Since supplier product data is constantly changing, we can keep measuring - obtaining a scorecard - and then tracking the changes in score.
By utilising data scorecarding, at any moment in time we can identify how a score is determined, right down to the finest detail. This is how a supplier can identify which data values need improvement.

For anyone conducting business process change, tracking scorecard change provides useful feedback in evaluating process improvement. Use it to test the effect of process change before undertaking expensive systems enhancement.

Data QA services can be used to ensure information meets criteria contained in standard rules. Other discrepancies, for instance in logistical information, are harder to detect. Wholesalers and retailers typically dedicate significant resources to re-measuring physical product attributes, to maintain the efficiency of their infrastructures. Suppliers can extend their own quality assurance programmes to include self-certification, which can then included as part of the product data set.

Please read the Product Overview section which explains how ProAdvance Data Quality helps you improve and maintain your product data quality.