In the course of digitalization, organizations today have very extensive and ever-growing quantities of data at their disposal (keyword: "Big Data"). However, empirical evidence shows that the data analyzed and used is often characterized by low data quality - even in internal company customer databases, on average around 30% of the stored data values are incorrect. In this way, an average American company, for example, incurs additional costs amounting to 15 million dollars annually. However, lack of data quality is not only a major problem in companies - in times of "fake news", the need for reliable information is also increasing in politics and society. Therefore, quantitative methods for measuring, controlling and improving data quality are needed.

#data quality #data quality #fake news

Projects

• Data quality in the automotive industry
• Data Quality in User-Generated Content (DQNGI)
• DQMM
• DQMM@Wiki