Often enough, data is referred to as the new oil. Most of the time, the companies are richly endowed with the raw material. However, the treasure trove of data is not being raised, as the topic is neither given sufficient attention in the company nor is the necessary data quality paid attention to. Ultimately, the hoped-for data booster cannot be ignited.
Often enough, data is referred to as the new oil. Most of the time, the companies are richly endowed with the raw material. However, the treasure trove of data is not being raised, as the topic is neither given sufficient attention in the company nor is the necessary data quality paid attention to. Ultimately, the hoped-for data booster cannot be ignited.
The German classic on data and information quality, which has just been published in its 5th edition by Springer-Verlag, illustrates how important the quality of the data is (see: https://www.springer.com/de/book/9783658309909 ) . The basic book is considered the first German-language book on data quality and has been supported 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 everyone 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 the data quality is to be measured in the company, the book shows simple solutions. The book is divided into three areas: basics, methods / tools and organization. Above all, the organizational part is of high practical relevance.
This part also describes the topic of data quality in connection with the Global Data Synchronization Network (GDSN). In chapter 25 (see: https://www.springerprofessional.de/gewaehrleistungs-einer-hohen-artikelstammdatenqualitaet-im-global/18605124 ) you will find a simple explanation of the GDSN as well as a detailed description of all of its components that are included in the daily Working with article master data can be used to optimize data quality, for example in the area of e-commerce. The so-called Data Quality Framework (DQF), the various implementation guidelines of GS1 for data quality via the atrify data pool and approaches of Data Quality Excelence (DQX) via Smart Data One should be mentioned in particular.
The use of the GDSN standard - as the corresponding chapter in the book makes clear - can be used in the company as an initial spark for a data quality offensive. In this way, the treasure trove of data can be easily raised and the "rockets of the data booster" ignited in terms of data quality. atrify will be happy to support you with this.
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