Most data quality software techniques – the common ones being data standardization, verification, and matching – were originally designed for customer data. Yet, product data differs sharply from customer data in structure and content. So, tools and techniques built for customer data’s predictable syntax and patterns (expressed mostly in structured data) rarely adapt well to product data’s variable syntax and nonstandard data values (commonly expressed in unstructured data). Hence, there’s a need to redesign standard data quality techniques – and design new ones – that address the unique requirements of non-customer data domains, especially product data.
What you will learn:
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