PROBLEMS AND CHALLENGES OF DATA ANALYTICS IMPLEMENTATION IN THE LOGISTICS SECTOR OF UKRAINE

Data analytics is a vital component of modern logistics management in Ukraine. It relies on methods and tools for processing, analyzing, and interpreting large volumes of data with the aim of discovering useful relationships, patterns, and peculiarities of the functioning of logistics systems. One of the key aspects of data analytics is data collection and processing. This requires having a structured database that can be accessed to obtain the necessary data. The data collection process may involve various sources such as logistics systems, reference information, electronic databases, and more. After collecting the data, it is essential to process and prepare them for further analysis. Another crucial aspect of data analytics is data analysis and interpretation. Various methods and tools are used for this, including statistical analysis, machine learning, analytical models, etc. Data analysis enables the detection of trends, dependencies, and hidden information that can be valuable for decision-making in logistics processes. Data interpretation involves seeking meaning and significance of the obtained results and converting them into practical recommendations and strategic decisions. The importance of data analytics in logistics processes cannot be overstated. In today's environment, when the Ukrainian market is becoming increasingly competitive, the ability to analyze information quickly and efficiently can be a decisive factor for success. The third key aspect is the use of the data obtained. After analyzing and interpreting the information, companies must be able to implement the knowledge gained into their logistics strategies. This may include optimizing supply chains, improving customer experience, or introducing new technologies. Data analytics plays a key role in modern logistics management. It allows companies not only to understand the current state of the market, but also to predict future trends, adapt to changes, and remain competitive. To use data analytics effectively, organizations must invest in developing their analytical capabilities, including staff training, implementing the latest technologies, and creating effective mechanisms for collecting and processing data.