In the digital economy, data assets have come to be regarded as the new oil, underscoring their critical role in modern business models and decision-making processes. In response, the Chinese government has prioritized the formalization and management of data assets, introducing policies aimed at enhancing their value. Given the unique nature of data assets, characterized by the potential for both depreciation and appreciation, precise methods for assessing value changes and realizing the appreciation of data assets are urgently needed. Effective data governance techniques, including data cleaning, acquisition, and integration, are essential for maximizing the economic potential of data assets. Against this backdrop, this survey explores two key issues from a data governance perspective: the enhancement of data asset value and the quantification of its changes. It is structured around two primary dimensions: first, by examining data assets’ inherent properties and quality indicators, and second, by utilizing an “on-demand evaluation” approach that assesses value of data assets in response to the performance of downstream machine learning models. By advancing understanding of these issues, this study seeks to optimize strategies for maximizing the economic impact of data assets through refined data governance practices.

Data cleaning is considered as an effective approach of improving data quality in order to help practitioners and researchers be devoted to downstream analysis and decision-making without worrying about data trustworthiness. This paper provides a systematic summary of the two main stages of data cleaning for Internet of Things (IoT) data with time series characteristics, including error data detection and data repairing. In respect to error data detection techniques, it categorizes an overview of quantitative data error detection methods for detecting single-point errors, continuous errors, and multidimensional time series data errors and qualitative data error detection methods for detecting rule-violating errors. Besides, it provides a detailed description of error data repairing techniques, involving statistics-based repairing, rule-based repairing, and human-involved repairing. We review the strengths and the limitations of the current data cleaning techniques under IoT data applications and conclude with an outlook on the future of IoT data cleaning.