Ejup Rustemi1* Mefail Tahiri2
1University of Tetova, North Macedonia
2University of Tetova, North Macedonia


Our society is data-driven. The digital revolution, where enormous amounts of data are produced daily, is the result of the hunt for novel solutions and competitive advantage. The processing and analysis of this vast data set, gathered from various sources and comprising inconsistent, inaccurate, insufficient, and deceptive information, presents significant challenges for today’s businesses. Due to the variety of data sources (such as the combination of text, graphics, audio, and video) and the speed at which real-time data is generated, gathered, and processed, these databases include a tremendous lot of complexity. In order to assess this massive amount of structured and unstructured data, new data analytics patterns and trends are required. Big Data is a vast collection of information-rich data that may be roughly categorized into five dimensions: I volume: describing the data’s substantial size; I velocity: the rate at which new data is generated and analyzed in real time; (ii) variety: representing unstructured data from heterogeneous sources such as traditional databases, transactional systems, the Internet, and social media; (iii) veracity: the authenticity or origin of the data; and (iv) value: the value of the data, which depends on whether the data collected is old or recent. This paper will emphaisze the importance and challenges of data science inside the business landscape.

 Keywords: data, data science, business, strategy, big data.

Volume 8. No.1(2023)

ISSN 2661-2666 (Online) International Scientific Journal Monte (ISJM)
ISSN 2661-264X (Print)



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This is an open-access article under the CC BY-NC-ND license (