| Abstract: | This master's thesis examines business reporting and data visualization in the era of artificial intelligence. The starting point of the thesis is that organizations have access to increasingly large amounts of data; however, data alone do not ensure high-quality decision-making. Data become useful only when they are properly collected, processed, integrated, analyzed, presented, and interpreted within a business context. Therefore, the thesis focuses on the importance of high-quality data, clear reports, appropriate performance indicators, and transparent visualizations for understanding business information.
The thesis presents the development of business reporting, the architecture of business reporting systems, different types of reports, the fundamental principles of effective reporting, and the concept of business intelligence. A significant part of the thesis is devoted to data visualization. Examples of both good and poor practices are discussed, including the inappropriate use of charts, overloaded visual displays, misleading use of colors, pictorial elements, and unclear tables. For each example, more appropriate alternatives are proposed, such as simple bar charts, clearer color scales, and more transparent report structures. In this way, the thesis demonstrates that the way data are presented directly affects the understanding, interpretation, and usefulness of information.
Special emphasis is also placed on artificial intelligence and its impact on business reporting. The thesis explains the differences between artificial intelligence, machine learning, deep learning, and generative artificial intelligence. At the same time, it highlights the risks associated with the use of artificial intelligence, particularly the possibility of incorrect or unverified results, dependence on the quality of input data, and the need for human oversight.
Based on the reviewed literature, theoretical foundations, and analyzed examples, the thesis concludes that effective data visualization and interpretation improve the understanding of business information and support higher-quality decision-making. In contrast, poorly designed reports and ineffective visualizations may lead to misinterpretations, even when the underlying data are accurate. Artificial intelligence can significantly enhance business reporting, but only when it is based on high-quality data, verifiable sources, clear methodological foundations, and responsible professional judgment by users. |
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