企业数据资产管理系统（EDAS）

企业数据资产管理系统（EDAS）采用基于“U-EDAS”数据资源与数据资产评估模型，实现企业数字资产“一本账”，辅助企业针对数字资产进行自主登记及动态管理，支持资产登记、资产评估、质量监测、数据看板等功能模块。该系统对企业数字资产进行认证，覆盖数据采、存、算、管、用各环节，推动资产“时效性”流动，提高全生命周期的数字资产质量，夯实资产底座。围绕指标模型，建立数据资产的自动评估，评估任务完成后形成评估报告，并通过直观的图表展示企业数据资产数量、分类统计等情况，以及重要数据资产分布情况，提供数据资产全局化的总览。该系统采用前台门户、中台管理和后台对接三层架构：前台门户主要包括统一的企业数据资产门户网站，对外提供数据资产查询、数据资产查看、数字资产监控等服务，实现用户统一访问认证和授权控制。中台是系统的核心业务层，包含企业主体登记管理、数字资源探查调研、数字资源目录管理、数据规范化入表、资产评级划分、资产合规评估、数据质量评价、数据资产增值开发等模块。通过这些模块构建起对数据资产的全生命周期管理和治理体系。后台主要通过开放接口与企业内部系统对接，实现ERP、CRM、SCM、OA、BI等系统的数据集成，将分散在企业原业务系统中的数据规范化汇总到中台资产库，为中台资产管理提供数据支撑。三者相互配合、有机衔接，共同构成连接数据源、资产管理、数据服务应用的闭环流程，使企业数据资产实现统一的标识、规范化治理、质量控制、合规审计和价值增值。客户价值：一、提供一站式的资产搜索引擎，实现对企业数字资产的集中化展示，企业数据资产清单包括应用目录、数据目录、基础设施、项目目录、企业知识产权和企业解决方案；资产运营分析看板汇总各类数字资产的关键监控指标，实现对企业资产状态的直观式监测，提高资产的数据可用性和商业价值。二、用于录入企业的基础信息，包括企业的统一社会信用代码、企业名称、成立时间、注册地址、法定代表人等与企业主体身份直接相关的信息，还需要记录企业的组织架构，包括总部及各个分支机构、业务部门和岗位信息，以及企业的主营业务范围、经营模式和各项业务活动的详情。企业主体信息的准确登记，有利于后续对数据资产进行全面和规范的登记管理。三、实现对企业存量和新增的数据资源的全面发现与收集。通过自动化的采集方式搜集可能包含数据资产的各类系统、数据库、文件等不同的数据源，发现和确认这些数据源中的有价值数据资产，探查到的结果可以用为数据资源目录的规范化建设提供依据。此外，数据资源探查还需要建立常态化的机制，以保证对数据资产的持续发现，并评估探查工作的全面性和有效性。通过数据资源探查，可以持续发现企业新增与遗漏的数字资产，丰富企业的数据资源目录。四、对企业拥有的数据资源进行标准化登记和目录化管理。需要建立数据资源的标识体系，并按照统一的模板对各类数据资源进行结构化收录，记录数据的基本信息、来源、数据范围等关键元数据。还需要支持资源之间的关系链接与血缘追溯，建立版本管理，记录目录的变更历史。支持设定不同用户的访问权限，保证目录信息的安全。通过建立数据资源目录，可以实现对企业数据资产的规范化登记和集中化管理，提高数据资源的可查性、可用性。五、对企业核心业务数据进行标准化建模和存储。针对不同类型的数据制定统一的入表规范，如对基础类参照数据、核心业务经营数据和创新项目数据等进行规范化建模和入表。入表体系需要确保数据结构完整性，并建立唯一标识码与编码体系。通过源数据的规范化入表，可以提高核心业务数据的质量，使企业系统基于同一数据基础运行，并支持数据的集中管理、交换与利用。六、通过第三方机构，对企业重要数据资产的权属、质量和价值进行确认，并进行区块链存证。评估需要第三方机构收集数据资产的来源和用途证据，进行权属分析和判断，明确企业对数据资产的所有权或使用权，并出具法律效力的确认意见，解决企业数据确权问题。此外还需要对数据资产的质量和业务价值进行评定，确定不同级别的数据价值。之后可在区块链等技术上实现资产元数据和评估结果的电子签名和上链存证，维护数据的确权确质确值。通过第三方评估确认和区块链技术保障，可以解决企业数据资产确权确质确值问题，降低数字资产合规风险。七、通过引入行业数据质量标准和评估模型，建立科学的质量评价指标体系，对企业数据资产的质量状态进行测评、诊断。构建数据质量评估指标库，包括准确性、一致性、完整性、规范性、时效性、可访问性等，并设计检测方法。在此基础上定期或按需对数据资产开展评估，生成质量评测报告，分析数据质量问题。另外，可以基于质量评级结果，进行数据资产的定价，确定不同层级数据的经济价值，实现数字资产报价。通过标准化质量评估和资产定价，可以有效管理数据资产质量，最大程度发掘数据价值。八、通过对数据的加工处理，提升数据资产的业务价值和经济价值。需要对数据进行清洗、补全、关联等提质工作，从数据中提取价值信息，生成洞察报告。也可以将数据转换成更适合业务分析的结构化数据或标准化模型。此外，需要研究不同场景下的数据需求，设计数据产品，更好满足决策需求。通过数据资产增值服务，帮助企业在现有数据基础上创造更大的商业价值，实现数字资产的增值。

The Enterprise Data Asset Management System (EDAS) adopts the "U-EDAS" data resource and data asset evaluation model, enabling enterprises to build a "unified ledger" for digital assets, assisting enterprises in independent registration and dynamic management of digital assets, and supporting functional modules such as asset registration, asset evaluation, quality monitoring, and data dashboards.

This system authenticates enterprise digital assets, covers all links of data collection, storage, computing, management and application, promotes the "timeliness-oriented" flow of assets, improves the quality of digital assets throughout their full life cycle, and consolidates the asset foundation.

Focusing on indicator models, automatic evaluation of data assets is established. After the evaluation task is completed, an evaluation report will be formed, and the number of enterprise data assets, classified statistics, and distribution of important data assets will be displayed through intuitive charts, providing a global overview of data assets.

The system adopts a three-tier architecture of front-end portal, middle-end management and back-end docking:
The front-end portal mainly includes a unified enterprise data asset portal, which provides services such as data asset query, data asset viewing and digital asset monitoring externally, and realizes unified user access authentication and authorization control.
The middle-end is the core business layer of the system, including modules such as enterprise main body registration management, digital resource exploration and investigation, digital resource directory management, standardized data entry into tables, asset rating division, asset compliance evaluation, data quality evaluation, and data asset value-added development. These modules build a full life cycle management and governance system for data assets.
The back-end mainly connects with enterprise internal systems through open interfaces to realize data integration of systems such as ERP, CRM, SCM, OA, and BI, and standardize and aggregate data scattered in the original enterprise business systems into the middle-end asset library, providing data support for middle-end asset management. The three parts cooperate with each other and are organically connected, forming a closed-loop process connecting data sources, asset management and data service applications, enabling unified identification, standardized governance, quality control, compliance audit and value appreciation of enterprise data assets.

Customer Value:
1. Provide a one-stop asset search engine to realize centralized display of enterprise digital assets. The enterprise data asset list includes application directories, data directories, infrastructure, project directories, enterprise intellectual property rights and enterprise solutions. The asset operation analysis dashboard summarizes the key monitoring indicators of various digital assets, realizes intuitive monitoring of enterprise asset status, and improves data availability and commercial value of assets.
2. Used to enter basic enterprise information, including information directly related to the enterprise's main identity such as the unified social credit code, enterprise name, establishment time, registered address, and legal representative. It also needs to record the enterprise's organizational structure, including the headquarters and various branches, business departments and post information, as well as the enterprise's main business scope, business model and details of various business activities. The accurate registration of enterprise main body information is conducive to the subsequent comprehensive and standardized registration and management of data assets.
3. Realize comprehensive discovery and collection of existing and newly added data resources of enterprises. Collect various data sources such as various systems, databases and files that may contain data assets through automated collection methods, discover and confirm valuable data assets in these data sources, and the exploration results can provide a basis for the standardized construction of data resource directories. In addition, a normalized mechanism needs to be established for data resource exploration to ensure continuous discovery of data assets and evaluate the comprehensiveness and effectiveness of exploration work. Through data resource exploration, newly added and missed digital assets of enterprises can be continuously discovered, enriching the enterprise's data resource directory.
4. Carry out standardized registration and directory management of the data resources owned by enterprises. It is necessary to establish a data resource identification system, and structurally include various data resources according to a unified template, recording key metadata such as basic data information, sources, and data scope. It also supports resource relationship linking and bloodline tracing, establishes version management, and records directory change history. It supports setting access permissions for different users to ensure the security of directory information. Through the establishment of a data resource directory, standardized registration and centralized management of enterprise data assets can be realized, improving the retrievability and availability of data resources.
5. Carry out standardized modeling and storage of enterprise core business data. Formulate unified entry specifications for different types of data, such as standardized modeling and entry of basic reference data, core business operation data and innovative project data. The entry system needs to ensure the integrity of the data structure and establish a unique identification code and coding system. Through standardized data entry of source data, the quality of core business data can be improved, enabling enterprise systems to operate based on the same data foundation, and supporting centralized management, exchange and utilization of data.
6. Confirm the ownership, quality and value of important enterprise data assets through third-party institutions, and conduct blockchain notarization. The evaluation requires third-party institutions to collect evidence of the source and use of data assets, conduct ownership analysis and judgment, clarify the enterprise's ownership or usage rights of data assets, and issue legally effective confirmation opinions to solve the problem of enterprise data right confirmation. In addition, it is necessary to evaluate the quality and business value of data assets and determine different levels of data value. Then, electronic signatures and on-chain notarization of asset metadata and evaluation results can be realized on technologies such as blockchain to maintain the confirmation of data ownership, quality and value. Through third-party evaluation confirmation and blockchain technology guarantees, the problems of enterprise data asset ownership, quality and value confirmation can be solved, reducing the compliance risks of digital assets.
7. Introduce industry data quality standards and evaluation models to establish a scientific quality evaluation index system, and evaluate and diagnose the quality status of enterprise data assets. Construct a data quality evaluation indicator library, including accuracy, consistency, completeness, standardization, timeliness, accessibility, etc., and design detection methods. On this basis, conduct evaluations on data assets regularly or on demand, generate quality evaluation reports, and analyze data quality issues. In addition, based on the quality rating results, the pricing of data assets can be carried out to determine the economic value of data at different levels and realize the quotation of digital assets. Through standardized quality evaluation and asset pricing, the quality of data assets can be effectively managed and data value can be maximized.
8. Improve the business value and economic value of data assets through data processing. It is necessary to carry out quality improvement work such as data cleaning, completion and association, extract value information from data, and generate insight reports. Data can also be converted into structured data or standardized models that are more suitable for business analysis. In addition, it is necessary to study data requirements in different scenarios and design data products to better meet decision-making needs. Through data asset value-added services, enterprises can be helped to create greater commercial value on the basis of existing data and realize the appreciation of digital assets.

搜集汇总

背景与挑战

背景概述

该数据集介绍了企业数据资产管理系统（EDAS），该系统实现数据资产全生命周期管理，包括登记、认证、评估等功能，采用三层架构设计，旨在提升企业数据资产的质量和商业价值。