Think about a product – it may have a size, weight, ingredient, dosage, or any number of data points that distinguish it from others available. Now think about consumers. Having this data at their fingertips is now an expectation when searching and buying products, in-store or online. If they can’t find the data, or it's incomplete, a sale could be lost. If it's inaccurate or out-of-date, processing a return could be costly, both financially and to your brand's reputation.
Targeting Data Quality
Understanding the power that lies within their data, Target, the Minnesota-based retailer, developed a model for robust data management with data quality at its core. With numerous product categories, Target has a massive amount of data to organize and maintain. As a result, they built their data quality strategy on a foundation of data governance, establishing an enterprise-wide methodology to guide how all data activities evolve.
This paper is a must-read for those managing product data. It provides an overview of the importance of data quality regardless of sector and shares insights on Target’s successful data quality journey for those interested starting their own.
