Problems and Solutions of Distributed Big Data Asset Right Management - GU Liping, ZHANG Xiaoyue (Nongye tushu qingbao xuebao, 2023)
[Purpose/Significance] Digital technology has become an important driving force in the utilization of data production elements. To make full use of data resources, the underlying accompanying problem is rights management. In practice, the ownership and the right to use of data resource are separated in the circulation process. It has become an urgent problem to conduct data resource management activities based on reasonable and legal protection of users' rights. [Method/Process] Based on the general framework of "practical basis - abstract analysis - general concrete understanding", this paper firstly identified four practice statuses of distributed big data assets as followings: 1) the application of new technology leading to facilitating platforms; 2) attention needed to paid to intellectual properties of multiple users' groups; 3) users' needs to be satisfied on various contexts. For example, conducting resource procurement, collection, data processing and layout of resource allocation systems based on cloud service platforms; 4) maintainance and management of high quality data sets, including self-built and imported resources, the negotiation of ownership and use rights, and emphasis on the rights strategies. Next, this study conducted abstract analysis on the generalized idea of basic dimensions of data rights management from four aspects, namely, technical resource regulation, reasonable use boundary, complexity of rights, and analysis of use rights. [Results/Conclusions] Based on the abstract analysis, this paper put forward and explained four successive solutions at data resource management institute level, as described below. 1) the principle and terms of data policy, which include but not limited to general statement, policies of content use, policy statement on metadata, social media policies, and terms of service; 2) the typical contexts and content planning of rights potofolio. This paper took the resource procurement business of academic libraries as an example to illustrate and summarize the four content planning items on rights portfolio: strategic plan, operational policies, data policies, end-user policies; and 3) the allocation management of data assets, which lays the key part of data asset management. In this paper, we consider that data asset management refer to description and management of the constituent terms of data assets and the relationship between terms. The cost structure of data asset management contains tangible cost (such as consulting, software and hardware purchase fee, and digital resources purchase fee) and intangible cost (such as indirect human resource costs, risks, social credit, and loss compensation); and 4) finally, the establishment standard, working process, associated contrasts and regulations, and the evaluation measurements of data asset management businesses at the institute level. Such solutions are proposed to provide some inspirations to data resource management institutes（such as libraries） under the distributed big data circumstance.