Companies increasingly recognize that the analysis of business information (business intelligence) can generate decisive competitive advantages. In addition, the compliance guidelines BCBS 239, Basel II and III, SOX, and Solvency II have led to legal requirements for a minimum level of quality in reporting and planning data and processes. The establishment of enterprise-wide data management thus continues to be one of the major challenges for IT and management in the years to come. Data quality is an integral success factor in the establishment of an optimal information infrastructure. A 2002 study from "The Data Warehousing Institutes" (TDWI) calculates that poor data quality in the US cost about $622 billion. Gartner market research stated in 2006: Poor data quality costs a typical organization 20% of revenue.... The worldwide financial and economic crisis after 2007 can retrospectively also be regarded as a data quality crisis. Despite far-reaching compliance requirements, many financial service companies have not been able to aggregate and prepare their risk data in a way to adequately control their risks, and they are still struggling in 2017. In the era of Big Data, data is viewed as the new oil and the available data volume worldwide multiplies every year. The requirements for transparency and data stream quality continue to increase, because these are considered essential for partially or completely new applications in decision support and other areas. But what use are larger data piles when quality and origin remain uncertain and when the costs for development and operation in data maintenance, integration, and analysis are proportional to the data volume? "Data quality is not everything, but without quality of data, it is all nothing." Metadata and metadata management are important aids for ensuring adequate data quality. The goal of this book is to take the current concepts, trends, and terms and help analysts and developers achieve data quality management.
