About Typical Errors in Representing Big Data and Digital Twins Made when Using Methods Mathematical Modeling

The work examines typical errors in the representation of big data and digital twins, which are made during their mathematical modeling, the structure of the error of mathematical modeling techniques and the mechanism of its formation. Recommendations are given for the presentation of large experimental data based on taking into account the errors of the initial indicators and modeling techniques. Calculating the error in representing big data as a percentage allows the author to quickly identify that it is often, when modeling processes, 1000 and even 100,000 times less than the error of the research methodology. The accuracy of data presentation in some cases can even be an order of magnitude greater than the accuracy of precision instruments used. The error of the modeling results is to a large extent determined by the error of the initial data used for calculations and modeling. A more significant component in the structure of the total error of the research methodology is the error in constructing the mathematical model of big data and digital twins itself, the value of which can range from ±10 to ±50%. Based on the actual permissible error of the measurement technique, recommendations are given on the degree of rounding of experimental data. The inadmissibility of direct readings from the screen of a personal computer is noted. For clarity, a number of examples of big data are provided, borrowed from the literature, in their original published form and after rounding them in accordance with the given recommendations. Keywords: mathematical modeling, error in the representation of big data, error in the modeling methodology, the mechanism of formation of the total error.