Research on Audit Risks in the Recognition of Data Assets on Financial Statements

This paper explores the audit risks associated with the recognition of data assets on financial statements,focusing on the complexities arising from their replicability,unique valuation patterns,and contextual dependencies.It identifies major misstatement risks at both the financial statement and assertion levels,including the potential for management to exaggerate data asset values,uncertainties in valuation methods,and deficiencies in data governance and internal controls.Additionally,auditors’lack of professional knowledge and inappropriate audit methods can lead to inspection risks.The paper emphasizes the urgent need for enhanced accounting standards for data assets,effective guidelines for their recognition and measurement,and robust internal controls.Furthermore,it advocates for the exploration of effective valuation methods and the incorporation of advanced technologies,such as big data and AI,into auditing practices.By improving auditor training and methodologies,organizations can better manage the inherent risks associated with data asset auditing.

Research on Integrating Data Assets Into Accounting Discipline System

As data is incorporated into production factors,the Accounting discipline should intensively study the new data asset.Starting from the analysis of the value attribute,right attribute,and relationship attribute of the data,it is found that under certain conditions,the data has complete Accounting attributes and can be included in the assets.The Accounting discipline shall establish the research direction of Data Asset Accounting and focus on the research,including the recognition and measurement of data assets,the value evaluation of data assets,information disclosure,and Data Asset Accounting standards.Data assets are facing the major challenge of integrating into the Accounting discipline.We can carry out the Accounting professional reform and textbook construction facing the practice of data assets management from the aspects of theoretical construction,talent training,and industry research cooperation.

An open dataset of data lineage graphs for data governance research

Data have become valuable assets for enterprises.Data governance aims to manage and reuse data assets,facilitating enterprise management and enabling product innovations.A data lineage graph(DLG)is an abstracted collection of data assets and their data lineages in data governance.Analyzing DLGs can provide rich data insights for data governance.However,the progress of data governance technologies is hindered by the shortage of available open datasets for DLGs.This paper introduces an open dataset of DLGs,including the DLG model,the dataset construction process,and applied areas.This real-world dataset is sourced from Huawei Cloud Computing Technology Company Limited,which contains 18 DLGs with three types of data assets and two types of relations.To the best of our knowledge,this dataset is the first open dataset of DLGs for data governance.This dataset can also support the development of other application areas,such as graph analytics and visualization.

Blockchain application in healthcare service mode based on Health Data Bank

Blockchain is commonly considered a potentialdisruptive technology. Moreover, the healthcareindustry has experienced rapid growth in the adoption ofhealth information technology, such as electronic healthrecords and electronic medical records. To guarantee dataprivacy and data security as well as to harness the value ofhealth data, the concept of Health Data Bank (HDB) isproposed. In this study, HDB is defined as an integratedhealth data service institution, which bears no “ownership”of health data and operates health data under the principalagentmodel. This study first comprehensively reviews themain characters of blockchain and identifies the blockchain-based healthcare industry projects and startups in theareas of health insurance, pharmacy, and medical treatment.Then, we analyze the fundamental principles ofHDB and point out four challenges faced by HDB’ssustainable development: (1) privacy protection andinteroperability of health data;(2) data rights;(3) healthdata supervision;(4) and willingness to share health data.We also analyze the important benefits of blockchainadoption in HDB. Furthermore, three application scenariosincluding distributed storage of health data, smart-contractbasedhealthcare service mode, and consensus-algorithmbasedincentive policy are proposed to shed light on HDBbasedhealthcare service mode. In the end, this study offersinsights into potential research directions and challenges.