Often enough, data is called the new oil. Most of the time, companies are richly endowed with the raw material. However, the treasure trove of data is not lifted because the topic neither receives sufficient attention in the company nor is the necessary data quality paid attention to. Ultimately, the hoped-for data booster cannot be ignited.
The importance of data quality is illustrated by the German classic on data and information quality, which has just been published in its 5th edition by Springer-Verlag (see: https://www.springer.com/de/book/9783658309909). The basic book is considered the first German-language book on data quality and has been edited since the first edition by the "data quality pope" Richard Y. Wang (Director, Chief Data Officer & Info Quality Program at MIT and Professor at UA Little Rock).
The book is recommended for anyone who wants to get an overview of the basics of data quality. It also provides an outline of the methods and tools for data quality management (DQM). If data quality is to be measured in the company, the book shows simple approaches. The book is divided into three sections: fundamentals, methods/tools and organization. Here, especially the organizational part is of high practical relevance.
This part also describes the topic of data quality in relation to the Global Data Synchronisation Network (GDSN). In chapter 25 (see: https://www.springerprofessional. de /good-quality-article-master-data-quality-in-the-global/18605124) you will find not only a simple explanation of the GDSN, but also a detailed description of all its components, which can be used in the daily work with article master data to optimize the data quality, for example in the area of e-commerce. Particular mention should be made here of the so-called Data Quality Framework (DQF), the various GS1 implementation guidelines for data quality via the atrify data pool and Data Quality Excelence (DQX) approaches via Smart Data One.
The use of the GDSN standard - as the corresponding chapter in the book makes clear - can be used in the company as the initial spark for a data quality offensive. In this way, the data treasure can be lifted in an uncomplicated way and the "rockets of the data booster" can be ignited in terms of data quality. atrify will be happy to support you in this.
