The top topic is data quality management (DQM).

The BI Trend Monitor 2020 of the Business Application Research Center (BARC) revealed that data quality management is one of the most important trends in the next few years.

According to this, the pure collection of information is subordinate to the quality and contextual reference of the information. The targeted application and interpretation of data will be the focus of business intelligence in the future.
 

Contact and improve data quality

Data quality analytics trends have grown rapidly over the past year. The development of business intelligence to analyze and extract information from the myriad of data sources we collect on a large scale brought a number of errors and low-quality reports: the differences between data sources and data types have made the data integration process more complex.

Master data management will become a central issue in companies' BI strategy. Only nowadays, many companies understand the impact of data quality on the analysis and further decision-making process, and therefore decide to implement a Data Quality Management (DQM) department or technique. In fact, data quality management is considered as a key factor for efficient data analysis as it forms the basis for further action. According to Gartner, poor data quality is estimated to result in an average loss of $15 million per year for organizations.

Markus Begerow

Data quality management consists of data collection, implementation of advanced data processes, effective distribution of data, and metadata management. Adherence to strict data quality standards also meets the standards of the latest compliance regulations and requirements. By implementing enterprise-wide data quality processes, organizations improve their ability to leverage business intelligence to gain a competitive advantage that enables them to maximize their return on BI investments.

Many business departments and managers are not yet truly aware that their data quality is in a much worse state than you know.

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