Analysis and Formulation of Compensation Strategies to Mitigate Data Error Costs by Investigating Various Error Categories

Abstract

Research on methods to standardize data error rates is critical because of the potentially disastrous consequences that data errors may have on businesses. In order to assist readers in developing an equalizations approach to handle data processing mistakes, this article explores the many shapes that these errors might take. The authors begin by differentiating between the two most prevalent types of data processing errors: random and systematic. Random errors, or mistakes that occurs by chance, could be mitigated by using more exact measurement techniques or a bigger sample size. Systematic errors, on the other hand, occur often and may have several sources, such as faulty equipment, inaccurate calibration, or bias in the data collection process. In order to address systematic errors, the authors propose an equalizations technique that comprises identifying and correcting the erroneous data sources. The idea behind this approach is to look for patterns in the data that might indicate systemic issues, and then to implement the appropriate fixes to mitigate such problems. Through a series of experiments utilizing both theoretical and practical data, the authors demonstrate the efficacy of their equalizations approach. Data error rates were significantly reduced in these experiments using the equalizations approach, enabling more accurate and trustworthy findings. In sum, the article effectively lays out the many problems with data processing and how to resolve them by using an equalizations approach. Data quality and decision-making skills may be improved and mistake rates can be reduced, which can lead to increased performance and success for organizations.