A lot has changed in the world of data in the last ten years. The amount of data is constantly growing and also the possibilities to gain so-called "insights" from data are getting better and easier. With the access to the cloud, the issue of scaling solution approaches for certain business areas became even easier and there was literally an eagerness to get more out of one's data.
A lot has also happened in data visualization and handling of interactive business dashboards. The rise of the "self-service analytics" field democratized access to data, and suddenly advanced analytics wasn't just for quirky analysts.
From 2018 to today, corporate management or corporate controlling has evolved significantly and the future of business intelligence and corporate performance management continues with new data trends. Companies' business strategies are becoming more customized in terms of planning, analysis and forecasting, and managers as well as controllers in their departments are interested in the best and most flexible solution for their specific business. The data must hum and that without errors!
Not to forget data quality management and data coverage, clean and secure data combined with a simple and powerful presentation. The multi-cloud strategy is also included, but the hype topics of machine learning and the use of artificial intelligence still have little practical relevance from the user's point of view.
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.
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.
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.