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The development of the Internet, IT technology and the struggle for greater profits, conquering new markets, scientific knowledge and many other trends in all spheres of life and work in the 21st century has caused a real revolution in the business and private environment. One of the consequences of such changes is the need to acquire, store and process huge amounts of data that are generated internally and externally from the business environment. As a result of these changes, public and private entities are forced to adapt to changes by implementing procedures and business processes in their business that will help them process and analyze large sets of structured and unstructured data. Those companies that implement and use Data Science systems expect to have a significant competitive advantage on the market. There are many tools for data analysis and processing, even whole programming languages. In addition to the many tools and programming languages, one of the most wellknown and popular programming tools that mainly visualize business data is Metabase. The most important thing for Metabase users is that almost all libraries and functionalities provided by this tool are free and have quality and maintained technical documentation. This paper will model and visualize time-based data from a business entity, preferably a large amount of data using the Metabase tool for data processing and visualization and to assess whether the tool can be used in today's current processes for data processing, as well as in Data Science and BI (Business Intelligence). The ultimate goal is the implementation of specialized procedures and systems for data analysis for better and more successful long-term strategic and operational decisions in the modern business environment and gaining a competitive advantage on the market. [ABSTRACT FROM AUTHOR]

The present study was aimed at investigating what are the people's levels of information about dietary fibre (DF) and how they relate to some factors that result from the people's perceptions associated with the knowledge about DF. The study consisted of a descriptive cross-sectional survey undertaken on a sample of over 6 thousand participants, original from 10 countries. Statistical techniques like factor analysis and cluster analysis were applied to the data. Factor analysis showed that 10 out of the 12 variables used to measure the knowledge about DF could be grouped into 2 factors: one associated with health effects of DF and the other with its natural sources. The internal consistency of both factors was evaluated by the Cronbach's alfa (0.854 and 0.644, respectively). Cluster analysis revealed that the participants could be divided into 3 groups: Cluster 1 - good knowledge both about sources and health effects of DF; Cluster 2 - good knowledge about the sources of DF but poor knowledge about its health effects; Cluster 3 - poor knowledge both about sources and health effects of DF. The results were subject to analysis of stability by splitting the data set in 2 halves. The cluster membership was found associated with living environment and level of education, but not with age, gender or country. [ABSTRACT FROM AUTHOR]