A particular group of systems that support the analysis and exploitation of knowledge sources are business intelligence solutions that incorporate modern forms of data storage, real-time querying, analytical, forecasting and data mining. These systems support managerial decision-making and help you evaluate organizational performance (business performance management solutions) and allow you to perform specific knowledge-intensive tasks. Systems supporting real-time analysis of large amounts of data play a key role. Data, web, and text mining continue to play a key role in generating knowledge. Data sources can be many: open data (e.g., social media, web resources, government portals), partially structured data (e.g., process description in XML), or unstructured data (e.g., text, blog post). One of the challenges of today is how to extract large amounts of fast-producing, analytical and commercially useful data from a diverse set of formats and sources. It also plays a key role in different areas of data mining and business analytics. Data mining is a process of discovering previously unknown features and relationships, primarily to exploit business benefits. Its field of application can provide a wide range of useful help in the prevention of IT misuse, in commerce (e.g., in related goods inspection), telecommunications (e.g., when examining viewership rates), insurance and banking (e.g., analyzing clients’ behavioral habits) and many other areas ( Kő, 2008). Intelligent systems in the field of artificial intelligence (text mining, expert systems, case-based systems, neural networks, genetic algorithms, and fuzzy systems) are still widely used, usually as components of a complex solution, often in data mining tools. Analyzing and exploiting solutions are often based on the abovementioned sources of knowledge and this overlapping usually results in better quality solutions. Such a project does not pose a challenge or change of culture for the stakeholders of the organization as they have already faced some of this during the introduction of basic systems and knowledge sources. In some case-by-case tasks, you can implement and use analytical, exploitative solutions even without the underlying systems, but in this case you need to be aware of the risks that such a project carries such as data quality issues.