Research And Design Of A Compensation Strategies Regarding Limiting Data Error Costs By Evaluation Of Various Error Types

Abstract

Organizations may suffer greatly from data mistakes, hence it's crucial that researchers find a way to equalize data errors so that they occur less often. This article delves into the many kinds of data processing errors and how to fix them using an equalizations technique. The authors start by classifying data processing mistakes into two broad categories: random and systematic. Improving measuring procedures or increasing the sample size might decrease the occurrence of random errors, which are mistakes caused by chance. However, there are several potential sources of systematic mistakes, such as equipment breakdown, calibration flaws, or data collecting bias, which manifest as errors that recur repeatedly. The authors suggest an equalizations strategy to deal with systematic mistakes; this strategy entails finding and fixing the data sources that cause inaccuracy. This method entails looking for trends or patterns in the data that might point to systematic mistakes, and then using the right corrective tools to fix them. By conducting a battery of tests with both synthetic and real-world data, the authors prove that their equalizations method works. Results were more accurate and trustworthy since the equalizations method drastically cut down on data errors in these trials. In sum, the essay sheds light on the many data processing mistake kinds and suggests a workable equalizations method for fixing them. Organisations may boost their performance and achieve greater success if they lower their mistake rates, which improves the data quality and allows them to make better judgments.